Biomedical Ontologies
Barry Smith
Preprint version of Chapter 5 of P. L. Elkin (ed.), Terminology, Ontology and their
Implementations, Springer Nature Switzerland AG 2022, https://doi.org/10.1007/978-3031-11039-9
We begin at the beginning, with an outline of Aristotle’s views on ontology and
with a discussion of the influence of these views on Linnaeus. We move from
there to consider the data standardization initiatives launched in the
nineteenth century and then turn to investigate how the idea of computational
ontologies developed in the AI and knowledge representation communities in
the closing decades of the twentieth century. We show how aspects of this idea,
particularly those relating to the use of the term “concept” in ontology
development, influenced SNOMED CT and other medical terminologies. Against
this background, we then show how the Foundational Model of Anatomy, the
Gene Ontology, Basic Formal Ontology, and other OBO Foundry ontologies
came into existence and discuss their role in the development of contemporary
biomedical informatics.
Keywords: biomedical ontology, Aristotle, Linnaeus, SNOMED CT,
Foundational Model of Anatomy, Gene Ontology, Basic Formal Ontology.
From Aristotle to Linnaeus
The term “ontology” (ontologia) is a neologism, coined in the seventeenth
century as an alternative to the Greek “metaphysics,” referring to the branch of
philosophy that is engaged in the study of what exists or of “what it is to be” in
the most general sense. An ontology in the modern sense—which includes the
biomedical ontologies discussed in what follows—is a taxonomy-like artifact
that is structured in such a way as to be useful not only to humans but also to
computers. The connection between the two senses of “ontology” turns on the
fact that the foundation of any taxonomy is the idea of what is general, and
this idea lies at the center of both ancient metaphysics and contemporary
ontology.
We are all implicitly familiar with the idea of what is general through our use
of general terms (nouns and noun phrases) in both everyday and scientific
contexts. In a tradition which goes back to Plato and Aristotle, such general
terms are said to refer to universals, where each universal is associated with
the collection of those particulars in reality which are its instances.1
A universal, sometimes also called a kind or type, is in some sense that which
all its instances have in common. Aristotle held that we acquire knowledge of
universals by observing the particulars that instantiate them. He himself
displayed a life-long interest in descriptive biology, and his metaphysics is
rooted in his experience of the world as a place populated by instances of
universals in the biological domain [1–3]. Science, from Aristotle’s point of
view—which he saw as “natural philosophy”—is thereby focused on what is
qualitative in nature, which is to say on describing how specific instances of
universals such as human or bird are at specific times running or sitting or
perspiring or rotting.
The process of mathematicization of science initiated by Galileo has of
course diminished the attraction of qualitative, descriptive views of science of
the sort embraced by Aristotle. But elements of such views still survive today,
not least in the world of biomedicine. Even the idea that we gain knowledge of
universals by examining their instances in reality is still very much a part of
science. This is because the terms scientists use in formulating scientific laws
are precisely terms representing universals. Laws link universals. And we gain
knowledge of such laws by performing experiments in which we engage with
the instances of the universals linked.
Science and Common Sense
Aristotle himself studied anatomy, astronomy, embryology, geography, geology,
meteorology, physics, and zoology.2 His starting point for exploring such domains
scientifically consists in establishing what kinds of entities they contain and thus
in mapping the corresponding domain universals. This implies also creating a
terminology consisting of the general terms with which to represent those
universals. Understanding “what it is to be” for the entities in a domain means to
acquire knowledge of what the particulars instantiating any given universal in
that domain have in common.
Aristotle assumed that human beings are in harmony with the world we find
around us in the sense that, when we observe reality, we are able to grasp in
our minds the universals instantiated by the things we see and touch. He does
not seek
“Particular” means: an entity that exists in some unique region of space and time. Where
universals are repeatable in indefinitely many instances, particulars are unrepeatable.
2
He also wrote on aesthetics, ethics, logic, metaphysics, government, politics, economics,
psychology, rhetoric, and theology [4]. In his article on Aristotle’s biology in the Stanford
Encyclopedia of Philosophy, Lennox [5] reports that in 1837 the anatomist Richard Owen
introduced a survey of Aristotle’s zoological studies by declaring that “Zoological Science
sprang from [Aristotle’s] labours, we may almost say, like Minerva from the Head of Jove, in
a state of noble and splendid maturity.”
1
deeper theories of what lies “behind” or “beyond” appearances, because to
seek such theories would be to assume that the world is not as it appears to be.
Certainly, there is room for error on the Aristotelian view. This relates, however,
to particular perceptions only; it leaves the general features of perceptual
knowledge untouched. Thus, the Aristotelian—like the commonsensical—way
of thinking about the world
will never concede that it is false throughout. Error is a local phenomenon, it does not
distort our entire outlook. Modern science, on the other hand (and the Platonic and
Democritian philosophies it absorbed) postulated just such global distortions. ([6], p.
148)
We return to this way of thinking in the section on “The FMA as a Canonical
Ontology”, when we investigate the idea of a canonical ontology and thereby
explore the reasons why biomedical ontologies have evolved in such a way as to
include elements deriving both from Aristotelian thinking and from modern,
data-driven science.
Aristotle on Definitions
The treatises compiled by Aristotle’s students and promulgated in his name
constitute a virtual encyclopedia of Greek knowledge. Typically, each treatise
begins with a review of earlier contributions to the study of the relevant
domain and then presents Aristotle’s own view with the aid of definitions of
one or more central terms. Book II of the Physics, for example, begins with the
definition of “nature” and Book II of On the Soul with the definition of “soul.”
The term “definition” is used in a variety of ways in the Aristotelian corpus,
but the core of his account rests on the idea that there is a hierarchy (or multiple
hierarchies) of more and less general universals, with each universal in the
hierarchy relating as species to its genus (in other words to its immediate
parent in the hierarchy). A species is then defined by stipulating what is specific
about the instances of its genus, which makes them instances of the species.
The way this works is illustrated in Fig. 5.1, which is based on the tree-form
representation of universals attributed to Porphyry, the author of an influential
introduction to Aristotle’s Categories from the third century (see [8]), which was
also the standard textbook in logic for at least a millennium after his death.
At the apex of the tree is the universal Substance, which divides into the
branches of Material and Immaterial. The former is then divided into Living
and Nonliving, and Living Substance is divided further into the Sentient and the
Non-sentient. Sentient Substance, finally, is divided into the Rational and the
Irrational, which then provides the means to define a Human Being as a
Rational Sentient Living Material Substance or—taking advantage of the way
in which definitions at lower levels incorporate logically the contents of
Fig. 5.1 Tree of Porphyry (from [7])
Definitions at higher levels—that a Human Being is a Rational Animal.3 This
feature of incorporation allows successively evermore complex thoughts to
be expressed by means of expressions that remain relatively compact at each
stage. It is exploited in mathematics and in all the sciences to enable human
beings to deploy ever more complex ideas in still understandable ways.
We can now understand why the Aristotelian rule for creating definitions is
referred to by Aristotle’s interpreters as definition per genus et differentiam.
What this means is that a definition of a species consists of two parts
specifying, respectively, its genus and an associated specific difference. The
instances of the species that is being defined are then all and only those
instances of the genus that satisfy the specific difference. We shall return to this
rule for formulating definitions in the section on “Aristotelian Definitions”
below.
In Aristotle’s Posterior Analytics, we find a definition of human being as “animal, mortal,
footed, biped, wingless” (92al-2) and alternatively as “animal, tame, biped” (96b31); in
Metaphysics Z “biped” and “animal” are said to “constitute the definition of man” (1038a3).
In his Politics (1.1253a) Aristotle defines an animal of the human kind as a zoon politikon, or
in other words as a social animal, an animal that naturally lives in a polis.
3
Aristotle’s Table of Categories
Aristotle created a number of more or less fragmentary examples of what we
would nowadays call domain ontologies. At the same time, he worked also on
creating a top-level ontology, which would bind those domain ontologies
together within a single logically and ontologically coherent framework by
providing a common starting point—a common set of highest genera—for the
definitions of their respective terms. The treatise compiled by Aristotle’s
students under the title Metaphysics addresses issues arising at this highest
level of generality.
Aristotle’s ideas on such matters are, as we shall see, still of considerable
influence today. But he was of course not the only philosopher in the ancient
world to have developed an influential approach to metaphysics. His
competitors in this respect included his own teacher Plato, who conceived
universals as inhabiting a timeless, ideal world, remote from the world of things
we encounter in our everyday experience. They included also Democritus, who
propagated the doctrine according to which all that exists are “atoms and the
void,” and Heraclitus, who embraced a process philosophy according to which
“everything is flux.” In the battle of grand theories, however, it is clear that
Aristotle was—at least until the seventeenth century—the overwhelming
victor. This was in part a result of the fact that Aristotelian metaphysics formed
the foundations of Christian theology. But it also reflects the relative
faithfulness of many of Aristotle’s ideas, when compared with those embraced
by his competitors, to much of what is accepted by common sense: that human
beings (for example) exist; that they grow and develop through time; that they
have qualities, habits, and dispositions of various sorts; that they stand to each
in various relations; that they occupy places; and so forth.
As will already be clear, one main starting point of Aristotle’s metaphysics is
the term “substance.” At Metaphysics 1030b6–12, for example, he asserts that “a
proper definition states the essence of an entity, by which is meant
‘substance’.” It would be a difficult task to provide here an account of what
Aristotle understood by this term. Suffice it to state here that in almost all the
contexts where he provides examples of substances, he refers to organisms (in
particular to humans and horses).
Substance is in Aristotle’s terminology one of the categories, which means
that it is one of those most general universals which go to form his top-level
ontology. They are what we call “primitives” in virtue of the fact that they
cannot be defined by the method of genus and specific difference because, lying
at the very top, they lack a genus.
In some places, Aristotle suggests that substance is the only category. There
is, however, another strain in Aristotle’s thinking according to which there are
multiple universals on this highest level. First is the category of substance,
which is marked by the fact that it and its subordinate universals are
instantiated in every case as a matter of necessity. What this means is that if a
being is, for example, a horse at any time in its existence, then it is a horse at all
times in its existence.
Second is a collection of accident categories, which are those highest-level
universals which hold of their bearer not essentially but as a matter of accident.
This means that they can be gained and lost during the course of the bearer’s
existence.
Aristotle presents different versions of his list of categories. Figure 5.2
presents the version described at Cat., 2a34-35, 2b3-5, 2b15-17, in which,
along with substance, nine accident categories are distinguished, examples for
each of which are (from Cat., 1b25-2a4):
Position: is lying, is sitting
Action: cutting, burning
Passion: being cut, being
burned
Time: yesterday, last
year
Quality: white,
grammatical
Quantity: four foot, five
foot
Relation: double, half, larger
Having: has shoes on, has armor
on
Place: in the Lyceum, in the
marketplace
Fig. 5.2 Aristotle’s table of categories in the arrangement proposed by Jansen [9]. Categories
are picked out in bold. The significance of the divisions marked by the italicized labels added
by Jansen will become clear in section “Basic Formal Ontology (BFO)”
This table should not be seen as being complete. Further categories can be
added to make the categorical structure of the world more explicit. Indeed, as
Jansen [9] points out, the project presented by Aristotle in the Categories “seems
to be rather a working report on an ongoing research project than something
ultimate and completed.” One candidate additional category might be that of
hole, an entity which can potentially be occupied or filled by something
material, as for example a womb may be occupied by a fetus, or a trench be
filled with water. In his treatise On the Parts of Animals, Aristotle refers to the
esophagus as “the channel through which food is conveyed to the stomach” (III,
3). At the same time, he refers to it as being “of a flesh-like character.” It is then
the flesh-like entity, rather than the channel, which forms a part in the sense
relevant to this treatise. This turns on the fact that Aristotle’s system of
categories has no clear room for places or locations which are not occupied, nor
for channels, or cavities, or voids—or, more generally, for holes which are not
filled (see [10] and section “Canonical Relations” below).
The table of categories in Fig. 5.1 may be extended also by recognizing
universals in addition to those which have particulars as their instances. On
some views, for example, there can be higher level universals, which themselves
have universals at lower levels as their instances. One example of such a higherlevel universal would be the universal universal [11].
Linnaeus’s Scala Naturæ and Genera Morborum
We can imagine Aristotle’s ten categories as forming the top of a much larger
hierarchy formed by universals belonging to successively more specific orders
of being at lower levels. This idea was elaborated in the doctrines of the Scala
Naturæ created by medieval philosophers, where each kind of entity is slotted
into its own proper place in what is seen as a Great Chain of Being. This in turn
formed one starting point for what we understand today as the tree of life, first
systematically documented in Linnaeus’s Systema Naturæ, the title of the 10th
edition of which is:
System of nature through the three kingdoms of nature, according to classes, orders, genera
and species, with characters, differences, synonyms, places [12].
Here, the “three kingdoms” are minerals, plants, and animals. In his Genera
Morborum, Linnaeus applied this taxonomical method to the realm of diseases,
distinguishing 11 classes, 37 orders, and 325 species of human disease, a small
selection of which is presented in Fig. 5.3.
Fig. 5.3 Fragment from Linnaeus, Genera Morborum [13], selecting from Orders 1–3 of the
5th Class (Mental diseases), extracted from Munsche and Whitaker [14]; see also Egdahl [15]
Physics
For some 2000 years after its basic ideas were first set forth by Aristotle and
his early commentators, the discipline of metaphysics advanced hardly at all,
to the degree that it formed the central part of what was habitually referred to
as philosophia perennis. The preeminent role of (Aristotelian) metaphysics in
the pantheon of philosophical disciplines began to be challenged, however,
from around the time of Descartes and Kant, who awarded pride of place to
epistemology, which deals not with being but rather with knowledge. At the
same time, the preeminent role of philosophy itself among the disciplines was
challenged by the rise of the empirical sciences.
During the eighteenth century, physics, in particular, evolved from its status
as a qualitative and primarily descriptive discipline to become a quantitative
and by degrees predictive discipline rooted in the mathematics-based study of
observational and experimental results. This led, however, to an increasingly
more urgent practical need for a standardized terminology for the
communication of such results in a way that would allow international
scientific and technological collaboration. Initiatives to address this need
culminated in 1875 with the ratification of the Metre Convention, a landmark
treaty which created the International Bureau of Weights and Measures
(BIPM). This led in turn to the SI international standard system of units, which
has served since 1960 as the universally accepted specification of the units of
measure for physical quantities.
The SI standard incorporates controlled vocabularies not only for the
representation of the physical units of measure but also for the kinds
(universals) of physical magnitudes for whose measurement these units are
employed. Both units and the corresponding magnitudes are divided into two
classes of base and derived (see Fig. 5.4), where the latter are defined in terms
of the former. Force, for example, is defined as mass times acceleration, and a
newton, the unit of force, is defined as the force needed to accelerate one
kilogram of mass at the rate of one meter per second squared in the direction of
the force applied.4
The success of the SI system is one important reason why the need for
ontology has made itself felt hardly at all in the domain of physics. But there is
a second, and no less important, reason, which turns on the fact that the kinds
of entities and relationships with which physics is concerned are rigorously
defined using mathematical equations.5 The language of mathematics thereby
serves in the physical domain as the lingua franca for the communication of
scientific knowledge. These two factors together effectively ensure the mutual
exploitability of both theories and results across all physical subdisciplines and
all application areas, a feature of the physical domain that has shown itself to
be indispensable to the success of all modern technology.
Similar advances in standardization of language and taxonomy were made
also in chemistry, beginning with Mendeleev’s Periodic Table in 1869 and
culminating in the work of the International Union of Pure and Applied
Chemistry (IUPAC). The latter has defined rules for naming and classifying
organic and inorganic compounds and created thereby a similar level of mutual
exploitability of chemical knowledge across all chemical disciplines, both pure
and applied. Comparable advances in biological standardization in the wake of
Linnaeus were primarily in the anatomical domain, with the Nomina Anatomica,
dating from 1895, replaced by the Terminologia Anatomica in 1998, though it
was clear already in 2001 that the latter left much to be desired from the
perspective of the new, information-driven approaches in medical science [19].
4
New definitions of the base units were proposed in BIPM [16]. Johansson points out in [17]
that these new definitions appear to be circular, given that symbols for what is to be defined
appear also in the defining expressions. He is indeed able to show that there are substantially
noncircular definitions underneath these circularities, but he also identifies certain
problems that still remain.
5
Less well defined are the terms used to represent both the different types of magnitude and
the ontological relations between (a) these magnitudes themselves, (b) the symbols
appearing in mathematical equations, and (c) the measurement results formulated in terms
of SI units. For a treatment of these matters, see Landgrebe and Smith [18].
Fig. 5.4 Examples of base and derived quantities and units in the SI system of units
Ontology, Logic, and Artificial Intelligence
Ontological Commitment
Before moving to the special case of biomedical ontologies, and to the
revolution in the handling of biomedical data which has come in the wake of the
human and other “model organism” genome projects, we need to take account
of the rise of ontology in the computer science disciplines, which occurred in
the closing decades of the last century. The beginnings of this episode in the
history of ontology can be traced to the 1948 paper “On What There Is” by the
prominent philosopher-logician Willard Van Orman Quine [20]. This paper
advances a new conception of the proper method of ontology, which at the
same time gave new respectability to the term “ontology” itself, not least
through its influence on the work of John McCarthy, creator of the term
“artificial intelligence.”
According to Quine, the ontologist’s task is to establish not what there is but
rather what are the kinds of entities to which scientists are committed in their
theorizing. The ontologist studies the world, on this conception, by drawing
conclusions from the theories developed in the natural sciences.
Each natural science has its own preferred repertoire of entities to the
existence of which it is committed, a repertoire which is revealed in its
vocabulary. In applying his method for identifying ontological commitments,
however, Quine turns not to the controlled vocabularies for physics or
chemistry established by organizations such as the BIPM or IUPAC. Rather, he
looks instead to a fictional future state of science, in which scientific theories
would have been subjected to a quite different sort of regimentation, resting
not on consistency in the use of terms but rather on the formalization of
scientific propositions using the language of first-order logic (FOL).
First-Order Logic (FOL)
FOL is the logical framework of choice that is used by philosophers and others
in the formalization of many different sorts of theories. It grew out of the
attempts by Frege—the founder of modern logic—and then by Whitehead,
Russell, and others, to use logic to formalize the whole of mathematics.
Building on the success of this work, Rudolf Carnap and other members of the
Vienna circle conceived the project of a “unified science” [21], based—in one
version at least—on the idea of a FOLbased axiomatization of all scientific
theories. In his The Logical Structure of the World [22], Carnap used logic as a
vehicle for the design of “linguistic frameworks supplying all of the (names for)
objects and concepts required by science” [23].
The use of FOL brings great benefits, including the following:
• It allows us to capture in a single formal system many features of our
reasoning not only in science and mathematics but also in our everyday
affairs.
• It has a mature and sophisticated model-based semantics, which is used in
all contemporary ontology applications.
• It exists in a number of semantically equivalent varieties of standardized
syntax optimized for specific uses, including computational use in support
of ontologies [24].
When it comes to computer applications, however, FOL has the shortcoming
that it is not decidable, which means there is no effective procedure for
determining, given a consistent set T of FOL formulas—which might, for
example, be the set of axioms of an ontology—whether an additional FOL
formula A can be added to T in such a way as to preserve consistency. As we shall
see in section “The Web Ontology Language (OWL)”, it was the attempt to
rectify this shortcoming which led to the development of OWL, the Web
Ontology Language.
Ontological Commitment Again
At the heart of FOL is the idea of quantified statements, for example of the form
∃xPx and ∀yQy, which mean, respectively, some value of the variable x satisfies
the predicate P and every value of the variable y satisfies the predicate Q. Thus,
“∃” stands for “for some” and “∀” for “for all.”
To determine the ontological commitments of a scientific theory formalized
in FOL, in Quine’s approach, means to determine which entities belong to the
ranges of those variables over which the formulas of the theory quantify—an
idea that is captured in Quine’s maxim: “To be is to be the value of a variable.”
Imagine, for example, that we wish to formalize in FOL the sentence
1. Teco is a bonobo
as part of the evidence base for a scientific theory. A typical FOL rendering of this
sentence is
2. ∃x(is-a-bonobo(x) & x = Teco)
or, translated back into English
3. there is some x which is a bonobo and which is identical to Teco
It is here Teco alone which is the value of a variable in either (2) or (3). This
means that it is to Teco alone that we are ontologically committed in making
either of these assertions.
But could we not reformulate (1) in such a way that, say, bonobohood would
serve as the value of a variable, for example by writing
4. ∃P(instantiates(P, Teco) & P = bonobohood)?
The problem here is that (4) is standardly interpreted as belonging not to first,
but rather to higher order logic, which is defined precisely by the fact that it
allows quantification over predicates.6 Quine’s use of FOL to determine
ontological commitment thus leads to an ontology in which only particulars
exist—in other words to a nominalist doctrine, according to which particulars
belong to the realm of what exists, but generals (universals) belong only to the
realm of what can be said.
For Aristotle, in contrast, as for his successors in the camp of what we shall
henceforth call “ontological realism” [26], there is in addition to Teco a second
something that exists, and that contributes to making true the sentence “Teco is
a bonobo,” namely some feature or way of being, some species or natural kind to
which Teco belongs, or some structure or pattern of DNA in Teco’s genome. From
the ontologically realist perspective, (1) then asserts a relation between Teco
and this second something.
The Vienna Circle Project to Unify Science
In a series of groundbreaking contributions around the turn of the last century,
Frege and others demonstrated that at least a large portion of mathematics could
be unified by showing that the corresponding mathematical truths are
ultimately truths of logic. The Vienna circle project was much less successful
[28]. But its underlying idea was influential nonetheless, above all through its
effect on the work of John McCarthy and others in the field of artificial
intelligence.
Serious problems, such as Russell’s paradox [25], arise in a logic, which allows unrestricted
quantification over predicates. Smith [26, 27] describes a simple paradox-free alternative
(nonstandard) FOL reading of sentences like (4), which involves quantification not over predicates
but over universals.
6
McCarthy was a leading figure in the first, logicist, or “symbolic” (also called
Good Old-Fashioned AI or “GOFAI”7) wave of AI research, contributing inter alia
to that strand in GOFAI, which sought to use FOL-based approaches (including
modal logic, situation calculus, and so on) in order to capture in a formal way
information about the world, for example to support the building of an
intelligent robot programmed with the ontology of common sense that is used
by humans.
It was in this context that McCarthy recognized the overlap between work
done in philosophical ontology and activity of building logical theories for AI
systems. McCarthy affirmed already in 1980 that builders of logic-based
intelligent systems must first “list everything that exists, building an ontology
of our world” [29]. This view, inspired by McCarthy’s reading of Quine,8 was
advanced also by McCarthy’s collaborator Patrick Hayes in his “Naive Physics:
Ontology for Liquids” [32], the first work to use in its title the word “ontology”
in the new sense of the term that is aligned to the use of computers.
As Hayes writes, looking back on the question of early uses of ontology in AI:
As far as I recall, my use in the title of the 1978 paper was original. I used it deliberately to
suggest/imply that the KR problem in AI was connected with philosophical ontology. The
background to this was my reading Carnap’s Logical Structure of the World as an
undergraduate, probably some time in 1964. Reading this blew my mind and first got me
excited about the idea of using logic to describe the real world. When I got into AI and
read McCarthy’s “Situations, actions and causal laws” … I was immediately struck by the
similarity both of goals and even in places of formal (what would now be called
‘ontological’) techniques. [33]
The Web Ontology Language (OWL)
The sort of ontology practiced by Hayes was of considerable influence, as is
illustrated for example in the Hobbs and Moore collection entitled Formal
Theories of the Commonsense World published in 1985 [34–36]. Work of this sort
in AI has of course been eclipsed in recent years by an approach centered around
deep neural networks and related stochastic approaches.9 In the world outside
AI, however, the work of McCarthy and Hayes was just one initial strand in the
burgeoning of work in ontology and knowledge representation (or “KR”) that
took place from the 1980s onwards, in a movement which received considerable
further impetus from the release, in 1999, of Protégé 1.0, a freely available
software tool for the building of ontologies (and applied not least in the
biomedical realm).
7
A term coined by Haugeland in [29].
To quote from McCarthy [30]: “In philosophy, ontology is the branch that studies what
things exist. W.V.O. Quine’s view is that the ontology is what the variables range over. Ontology
has been used variously in AI, but I think Quine’s usage is best for AI .”
8
At about this time, the drive to find a computationally tractable language for
the purpose of developing formal ontologies led to the exploration of subsets
of FOL, especially in the family of so-called description logics [38]. This
culminated in the standardization by the World Wide Web Consortium (W3C)
of the Web Ontology Language or “OWL” in 2004, which is currently the most
widely
used
logical
framework
for
ontology
development
(https://www.w3.org/OWL/).
The new ontology languages were optimized for computer use, though
unfortunately this came at the price of sacrifices in expressiveness [39, 40].
One result of the new ease with which ontologies could be built led accordingly
to an upswell of overlapping and often mutually inconsistent efforts, as
different groups sought in different ways to overcome the barriers of low
expressivity. The results are illustrated for example by the way in which the
fashion for agent-based modeling around the turn of the millennium led to the
development of some 30 “agent ontologies,” under headings such as action,
actions, activity, agent, agents, agent architecture, agent communication, and
agent framework.10
Many in the KR community seem to have assumed that the development of
many, many ontologies is something positive. It is necessary only that each of
the ontologies developed should be associated in the minds of its developers
with some potential use case—an idea promulgated for example by Noy and
McGuinness in their influential ontology manual [41] in their assertion that
“Deciding whether a particular concept is a class in an ontology or an
individual instance depends on what the potential applications of the ontology
are.”
The multiplication of ontologies derived also from the fact that during the
period in question grant funding was available for the development only of
novel ontologies. Efforts to establish the sorts of principles of best practice that
might point ontology in a more scientific direction were, on the other hand,
neglected. Ontology development in this period, not surprisingly, gained a bad
reputation—the results, it was said, were “brittle,” “unsustainable,” and
“unscalable” and rested on oversimplified (and thus often unscientific) models
of the relevant subject matters.
9
Something like GOFAI may be enjoying a mild reawakening, for example, in the recent book
on general AI by Marcus and Davis [37], where a logic-based framework of commonsense AI
is seen as a possible avenue to allow the gluing together of various narrow stochastic AIs
within a single general framework.
10
These are listed in the catalog of ontologies developed using DAML, the DARPA Agent
Markup Language (http://www.daml.org/ontologies/, last accessed July 30, 2021), which
was one of the precursors of the OWL language.
The Concept Orientation
The idea that we should seek to focus on the development of well-grounded
reference ontologies was rejected, in many circles, since it was taken to imply
that the authors of such ontologies would aspire to the possession of some kind
of God’s eye perspective. Since such a perspective is unavailable, it was assumed
that the best we can achieve is ontologies based on the ontological
commitments of specific languages, theories, systems of beliefs, or what we
shall encounter below as “conceptualizations.”
The discipline of knowledge representation has to deal, after all, not with
reality, but rather with the knowledge (and thus the concepts) in people’s
minds. It is thus very likely to lead to the situation in which there is a plurality
of ontologies covering the same topic, since there is plurality of knowers whose
knowledge is being captured and a plurality of uses to which this knowledge is
being put.
KR researchers who invested in trying to develop more generally applicable
methodologies focused their efforts especially on approaches to ontology
development based on model-theoretic semantics, making important
contributions not least to the development of the ideas underlying OWL [38].
There too, however, a presupposition to the effect often reigned that we can
never understand what a given language or theory is really about—thus we can
never compare an ontology to any independent reality beyond. We can, though,
build abstract (set-theoretic) models, which we can usefully manipulate, for
example in checking the consistency of a set of definitions and axioms.
From around 1990, there were, however, some few who acknowledged the
need for a common framework of high-generality terms, axioms, and
definitions, which would promote ontology reusability by building ontologies
in a way that would ensure correspondence to the things and processes in
reality they were designed to represent. Thus, they embraced the need for just
one agent/action ontology, just one software ontology, and so forth, and they
started to ask questions like: “What is an object/process/attribute/relation?
What is a transaction, a person, an organization? How do they depend on each
other? How are they related?” [42].
It was in this way that early examples of top-level ontologies began to be
developed with the goal of unifying and systematizing the development of
domain ontologies at lower levels, though at first with little traction in the
broader world of KR scientists in general or of ontology developers in
particular.
In the year 1993, Tom Gruber published the first credible attempt at
defining what an ontology—in the new, computer science sense of this term—
is:
An ontology is an explicit specification of a conceptualization. The term is borrowed from
philosophy, where an ontology is a systematic account of Existence. For knowledge-based
systems, what “exists” is exactly that which can be represented. When the knowledge of
a domain is represented in a declarative formalism, the set of objects that can be
represented is called the universe of discourse. This set of objects, and the describable
relationships among them, are reflected in the representational vocabulary with which a
knowledgebased program represents knowledge. [43]
Gruber’s definition was rapidly adopted in multiple subfields of computer
science. The definition still, of course, leaves open what a “conceptualization”
might be and how the crucial sentence “For knowledge-based systems, what
‘exists’ is exactly that which can be represented” is to be interpreted.11 What
can be said with certainty, however, is that the vast majority of those following
in Gruber’s footsteps assumed that his definition meant that an ontology is a
representation not of entities in reality but of concepts.
In the same year, on the other hand, the first International Workshop on
Formal Ontology in Conceptual Analysis and Knowledge Representation was
held in Padua, which brought together ontologists such as Gruber from the
computer science field with philosophers embracing a more traditional
approach to the understanding of the meaning of “exists.”12
The Case of SNOMED
The dominance of what is sometimes referred to in terminology circles as the
“concept orientation” made itself manifest especially in the rapidly expanding
field of medical terminology research, which is also the field in which the
problems associated with concept-based approaches have been deliberated
upon most persistently. The immediate need of medical terminologists was to
address the problems raised by the huge numbers of synonymous and quasisynonymous terms in the various medical disciplines. This problem is of minor
importance where only humans are involved in the application of medical
terms. Human experts in any given field know full well how to handle
synonymy. When computers enter the picture, however, matters are different.
The (for a long time) most influential solution to the problem of how
computers are to handle synonymy was presented by Cimino in his classic paper
on “Desiderata for Controlled Medical Vocabularies in the Twenty-First
Century” [46]. Cimino’s thesis is that, in the medical domain,
11
This sentence is, for someone with a background in philosophical ontology, deeply
problematic.
12
This event served as the launchpad for the subsequent FOIS (Formal Ontology in
Information Systems, https://iaoa.org/index.php/fois/fois-history/) conference series,
which remains the premier event in ontology (science). Its organizers, Nicola Guarino and
Roberto Poli, describe the meeting as “probably the first interdisciplinary initiative in this
area, aiming to explore the connections between philosophers belonging to the tradition of
Brentano and Husserl, philosophers of language, and people working on principles of
knowledge representation and engineering” ([44], p. 624). On Guarino’s unique
intermediating role in these developments, see his [45].
most systems that report using controlled vocabulary are actually dealing with the notion
of concepts. Authors are becoming more explicit now in stating that they need
vocabularies in which the unit of symbolic processing is the concept—an embodiment of
a particular meaning. Concept orientation means that terms must correspond to at least
one meaning (“nonvagueness”) and no more than one meaning (“nonambiguity”), and
that meanings correspond to no more than one term (“nonredundancy”).
The definition of “concept” here provided—as “an embodiment of a
particular meaning”—is, however, difficult to parse. Embodied in what?
Moreover, to understand this definition, we would need to understand already
the meaning of the term “meaning,” which philosophers have long recognized
as a difficult nut to crack.
The central problem not addressed by Cimino, however, is that the meaning
of the word “concept” itself varies drastically from one community to the next
(and sometimes from one paragraph to the next). Certainly, there are
acceptable and scientifically well-defined uses of this term, for instance in the
study of conceptual change in developmental psychology [47]. But
constructing a medical terminology—for example a gargantuan terminology
such as SNOMED CT13—is not an exercise in empirical psychology.
Unfortunately, SNOMED CT is itself pervaded (quite literally from top to
bottom) by the concept orientation, as is seen in the fact that SNOMED CT
concept is the topmost node of the entire SNOMED CT taxonomical hierarchy
and thus subsumes the entire SNOMED CT universe. Thus, we have:
clinical finding is_a SNOMED CT Concept
environment or geographical location is_a SNOMED CT Concept,
pharmaceutical product is_a SNOMED CT Concept,
social context is_a SNOMED CT Concept,
and many more. Given, therefore, the standard reading of is_a—the reading
accepted in other contexts by SNOMED CT itself—it follows that when you are
suffering from a headache then what you are suffering from is_a (is a) SNOMED
CT concept.
The SNOMED CT community does indeed go to great lengths to explain what
it means by “concept,” for example in its Editorial Guide from 2018,14 where we
read:
Formerly known as the “Systematized Nomenclature for Medicine,” though this label has
been dropped since the SNOMED International Organization no longer sees SNOMED as a
nomenclature. “CT” stands for “clinical terms.” The first versions of SNOMED were developed
under the leadership of Roger A. Côté, originally under the name “Systematized Nomenclature
of Pathology” (SNOP). When Côté visited the Vatican to present to the Vatican Library a copy
of the four volumes of what was then titled SNOMED International: The Systematized
Nomenclature of Human and Veterinary Medicine, he also met Pope John Paul II, who is said
by some to have remarked, “Do you realize that this spells ‘DEMONS’ backwards?”
13
5. SNOMED CT concepts should name classes of things.
6. A concept is defined as a clinical idea to which a unique concept identifier
has been assigned. Concepts are associated with descriptions that contain
human-readable terms describing the concept.
7. A term is defined as a human-readable phrase that names or describes a
concept.
We note that in (5) concepts are identified as names, while in (6) they are
identified as clinical ideas and explicitly distinguished from terms. In (7),
terms are identified as names or descriptions of concepts.15 Thus, the term
“Myocardial Infarction” (for example) describes not myocardial infarction (the
clinical phenomenon that appears in certain patients) but rather the concept
Myocardial Infarction. Terms in medical terminologies à la Cimino are about
Cimino’s “embodied meanings.”
Already in 2010, SNOMED CT had responded to criticisms concerning the
problems created by such ambiguities in its use of the term, by publishing as
the Glossary entry for “Concept” in its Technical Reference Guide the following
warning:
Concept
An ambiguous term. Depending on the context, it may refer to:
•
•
•
a clinical idea to which a unique ConceptId has been assigned;
the ConceptId itself, which is the key of the Concepts Table (in this case, it is less
ambiguous to use the term “concept code”);
the real-world referent(s) of the ConceptId, that is, the class of entities in reality
which the ConceptId represents (in this case, it is less ambiguous to use the term
“meaning” or “code meaning”)
But as Ceusters [49] notes, merely pointing out this problem does not imply
that the problem has been solved. Indeed, the very same Glossary still contains,
for example, an assertion to the effect that a SNOMED CT term is “a text string
that represents the Concept.”16
So what is it then that is represented by a term: (1) the clinical idea, (2) less likely, but
nevertheless in line with the expressed ambiguity—the ConceptID, or (3) the real-world
referent(s)? The same question must then be asked for the several hundred occurrences
of the word ‘concept’ throughout the SNOMED CT documentation. In some cases, readers
can infer from the context which meaning is intended, but in most cases, only the SNOMED
CT authors can provide the answer by rewriting the entire documentation. ([48]; see also
Sect. 2.1 of [50])
14
https://confluence.ihtsdotools.org/download/attachments/75337342/doc_EditorialGuide_Current-en-US_INT_20180731.pdf/
15
Friends of the concept orientation can of course criticize those who use the term “term” as
an alternative to “concept” by pointing out that the term “term,” too, can be misused in a way
that involves a blurring of the distinction between “entities of the domain” and “entities of
language.” This occurs, for example, when Bauer et al. [48] describe a new software tool
called Ontologizer as “a Java application that can be used to perform statistical analysis for
overrepresentation of Gene Ontology (GO) terms in sets of genes or proteins derived from
an experiment.”
The most recent SNOMED CT documentation reveals no advance on this
front, suggesting now that “concept” and “term” are interchangeable and
introducing an entirely new characterization of concepts as “clinically relevant
thoughts”:
Concepts, or terms, are represented by unique codes and human readable descriptions.
Each concept is a unique clinically relevant thought, across a wide range like abscess,
zygote,
measurement
procedure,
or
substance,
as
examples.
(https://www.imohealth.com/ideas/article/snomed-ct-101-a-guide-to-theinternational-terminology-system/)
Curing an abscess, then, means curing a clinically relevant thought.
First is what we might call the argument from intellectual modesty, which can
be summarized as follows: It is medical domain experts who must answer for
the truth of whatever theories the medical terminology is intended to mirror.
Since domain experts themselves will sometimes disagree, any given
terminology should embrace no claims as to what the world is like, but reflect,
rather, some abstract conceptual substitute derived, somehow, from the
different concepts used by different experts.
Against this, however, it can be pointed out that communities of experts
working on common domains in the medical as in other scientific fields in fact
accept a massive and ever-growing body of consensus truths about the entities
in these domains. Where conflicts do arise in the course of scientific
development, these are highly localized and pertain in medicine primarily to
specific mechanisms, for example of drug action or disease development. But
the latter can serve as the targets of conflicting beliefs only against the
background of a large body of shared presuppositions.
Moreover, we can think of no scenario under which it would make sense to
postulate special entities called “concepts” as the entities to which terms
subject to scientific dispute would refer. For either, for any such term, the
dispute is eventually resolved in favor of one side or the other, and then it is the
corresponding real-world entity that has served as its referent all along. Or it
is ultimately established that the term in question is non-designating, and then
this term is no longer a candidate for inclusion in whatever is the active version
of the relevant terminology.
The proposal from SNOMED and other defenders of the concept approach,
however, is much more radical. It is that we provide guaranteed referents
called “concepts” not only for terms identified as problematic but also for
every single term in the terminology. The realist alternative solution is in
contrast more modest. It is simply to treat any terminology as subject to a
process of evolution [56, 57]. Even terms still subject to dispute can be
incorporated into the terminology alongside other terms already accepted as
17
See for example Ceusters et al. [50, 51]; Bodenreider et al. [52]; Bona and Ceusters [53];
Ceusters and Mullin [54]; and Guarino et al. [55].
referring to real-world entities, but in such a way that they are marked as being
still subject to dispute and thereby treated logically as unavailable for use in
certain sorts of inferences.18
Another argument in favor of the concept orientation is the argument from
negative findings. Consider, for example, the case where a clinician reports a
finding of “absent nipple.” The defender of the concept orientation will argue
that there is no real-world entity denoted by this expression, and therefore that
the expression must refer to something like a concept. Certainly, clinicians need
to record such findings. But from the realist point of view, their findings are
precisely that a nipple is absent, not that a special kind of (“absent,” conceptual)
nipple is present [59].
Next is the argument from hypertension. The subject matters of biology and
medicine are, as it is held, replete with entities which do not exist in reality but
are rather convenient fictions, as in the case of the entities designated by
expressions such as “hypertension” or “obesity” or “abnormal curvature of
spine.” Such abstractions are, as it is held, “mere concepts,” since they reflect
not joints in reality but rather certain more or less arbitrary human decisions
(which may indeed vary over time).
From the realist point of view, in contrast, such terms are analogous to, for
example, “Poland” or “the Middle Ages.” That is, they represent full-fledged
entities in the real world, but they are entities whose boundaries are precisely
the results of decisions made by human beings. The meter, the kilogram, and
the second, too, are the results of fiat demarcations of this sort, and so also is
hypertension, which rests on a (periodically readjusted) fiat threshold
established by consensus among physicians.
Finally, we can mention what we might call the argument from
administration, which asserts that, for many of the purposes for which medical
terminologies are devised, a focus on something like Aristotelian universals
would be far too restrictive. Consider the ICD (International Classification of
Diseases) term:
8. Tuberculosis of adrenal glands, tubercle bacilli not found (in sputum) by
microscopy, but found by bacterial culture.
There is no ontological difference between tuberculosis diagnosed by
microscopy and tuberculosis diagnosed by bacterial cultural, any more than
there is such a difference between tuberculosis diagnosed on a Wednesday
and tuberculosis diagnosed on a Thursday, or while wearing socks. For the
administrative purposes of the ICD and its many users, however, it is important
that differences such as those expressed in (8) should be accounted for
terminologically.
Perhaps, then, a term like (8) should be acknowledged as representing a
concept? But no, and yet again: no. (8) is about tuberculosis; indeed, it is about
tuberculosis of adrenal glands (and thus it is also about glands) and similarly
18
Compare the strategy based on the Modal Relation Ontology outlined by Rudnicki [58].
it is also about sputum [60]. Wherever (8) occurs in any document prepared
by some clinician user of ICD, we can be sure that the author of this document
is quite clear in her mind that that is what this term is about. She is not using
this term to refer to, for example, someone’s clinical thought.
Combination terms like (8) involve the mixing together of properly
ontological terms (representing universals in the domains of disease, anatomy,
and species taxonomy) with epistemological terms relating to how particular
instances of a disease were discovered to exist, a matter of how, in this case,
reality is understood by health professionals. Manipulation of such
combinations is an indispensable part of information-driven medical research,
and so there is certainly no objection to developing ontologies whose terms
would capture distinctions such as that between a bacterial culture test and a
microscopy assay. Such ontologies are indeed already being developed (see for
example Bandrowski et al [62] and Gurcan [63]).
Needed, too, are ontological resources which allow the representation of
what we might think of as administrative aspects of medical or scientific
discourse. Consider a term such as:
9. Subject in clinical trial SwEaTB for Diagnosing of Acute Tuberculosis.
Here, we have a term that is not intended to represent a universal or the
extensions of a universal (in anything like the Aristotelian sense). Rather, it is
intended to capture what we can think of as a convenience combination (also
called “defined class” [64]). We then need to distinguish two kinds of ontologies:
what we might call “reference ontologies,” on the one hand (dealt with in the
sections on “The Foundational Model of Anatomy” and “The Open Biological
and Biomedical Ontologies (OBO) Foundry” below), which are designed to be
of global reach and application neutral and thus to capture universals, together
with, on the other hand, “application ontologies,” which result from the
combination of terms from reference ontologies together with terms such as (9)
developed for local, application-specific purposes [65]. Building this sort of
bridge between application ontologies and reference ontologies is by no means
a trivial matter [66]. Experience strongly suggests, however, that it is the only
course that will avoid the sort of destructive proliferation witnessed in the
ontology field in the 1990s.
The Foundational Model of Anatomy
From around 2013, a paradigm shift has been occurring in biomedical
terminology and ontology development circles [67] away from the concept
orientation. Attempts have since then been made for example to create an
ontologically robust upper-level structure for SNOMED CT [68]. The first
biomedical ontology to be developed in the spirit of ontological realism, however,
came much earlier. This was the Foundational Model of Anatomy [69], which
addresses the need for a generalizable anatomy ontology that could be used and
adapted by any computer-based application that requires anatomical
information. The FMA is a domain ontology that represents a coherent body of
explicit declarative knowledge about human anatomy. It has the potential for
enabling many digital applications involving reference to and manipulation of
information about anatomical entities, for instance in educational applications,
particularly in the domain of distance learning, and as the basis for computer
models, for example in the area of human anatomical development. Its
ontological framework can be applied and extended to all other species, and it
provides the template for CARO (the Common Anatomy Reference Ontology)
[70] and much of the content for the UBERON integrated cross-species anatomy
ontology [71].
The FMA as a Canonical Ontology
The FMA is very large, comprising some 120,000 terms and over 2.1 million
assertions of relationships between the entities represented by these terms. Yet
for all its size, it addresses only what we can provisionally think of as the normal
healthy human being. This is because the attempt to do justice to, for example,
all possible types of variants and pathologies affecting human anatomy would
lead to an explosion in size, which would make the result unmanageable and
probably also of little utility.
Rather, the strategy of the FMA is to constitute a canonical ontology, ranging
over types (universals) which are in a sense idealizations of the human
organism’s body and of its component parts. More precisely, the FMA
represents all material objects, all portions of substance, and all spaces that
result from the coordinated expression of the structural genes of the human
organism (in a good approximation: all parts of a normally developed human
body, from the macromolecular to macroscopic levels of granularity).
Canonical anatomy is thus distinct from instantiated anatomy, which
comprises anatomical data about individual organisms. Though it does not itself
comprise such data, the FMA serves as a valuable framework for capturing and
storing instancelevel anatomical data in computable form, by providing the
vocabulary for describing those ways in which instantiated anatomical
structures can depart from what is canonical [72].
Canonical Relations
To capture the meanings of its terms in a computer-parsable form, the FMA
ontology, like the other biomedical ontologies which have followed in its wake,
consists primarily of statements of the form “A rel B,” where “rel” stands for a
relational expression such as “constitutional_part_of,” “has_regional_part,”
“is_member_of,” “is_tributary_of,” and most importantly “is_a” (meaning either:
is a subtype of or is a subclass of), which is the relation used to determine the
backbone taxonomy of every ontology. The upper part of the FMA backbone
taxonomy is represented in Fig. 5.5. (“Anatomical Space,” here, refers to the
sorts of channels and cavities referred to in section “Aristotle’s Table of
Categories” above.)
Fig. 5.5 Upper-level structure of the Foundational Model of Anatomy (arrows express is_a
relations)
The now standard way of defining part_of and other such relations between
types in ontologies is by reference to the relations that hold between the
corresponding instances of these types and using the FOL device of
quantification. Two major types of definitions are then required, for relations
between types of processes, on the one hand, and between objects and their
parts and aggregates, on the other. For the former, we have:
X has_part Y = def. For any instance x of the process type X, there is some
instance y of the process type Y, which is such that y instance-level-partof x.
Example: Development of Spleen has_part Development of Splenic Lobules.
For the latter, however, we need to take account of time, in order to do
justice to the fact that objects can gain and lose parts while preserving their
identity:
X has_part Y = def. For any time t and for any instance x of the object type X
at t, there is some instance y of the object type Y at t, which is such that y
instance-level-part-of x at t.
Example: Set of Teeth has_part Left Maxillary Dentition.
On the basis of a set of definitions modeled on the above, a group of leaders
of different groups of biomedical ontology developers, including not only the
FMA and Gene Ontologies but also the GALEN group around Alan Rector,
developed the Relation Ontology (RO) [73]. This provides a basis for the formal
definition of the relations used by biomedical ontology developers in a way that
promotes interoperability of the ontologies, which use them and thereby allow
new types of automated reasoning both within and across ontologies.19
In some domains, universal parthood assertions of the abovementioned sort
are unproblematic. This holds for example for relations between molecules and
their parts in chemistry. It also holds for certain anatomical relations, such as
Neuron has_constitutional_part Plasma Membrane.20 In biology in general and
in medicine in particular, however, such universal assertions are problematic
because there are variants (for example, some humans have a middle lobe of
left lung) and pathologies (for example tumors, or missing teeth). Many
assertions of relations in the FMA hold, therefore, as a matter of canonical
ontology.
This means that an FMA statement such as
Skin of Thumb has_regional_part Nail of Thumb
is not an empirical assertion. Thus, it is not falsified by the existence of human
thumbs from which the nail has been removed. Rather, it is a statement that
expresses how Nail of Thumb and Skin of Thumb are supposed to relate to each
other in virtue of the workings of the underlying structural genes of the human
organism.
19
The current version of the Relation Ontology can be found at
http://www.obofoundry.org/ontol- ogy/ro.html. An expanded set of upper-level relations,
developed to deal with the problem documented by Grewe, et al. [74], is provided in part 2
of [75].
20
The two main types of part in FMA are constitutional parts, which are genetically
determined, as in Hand has_constitutional_part Skeleton of Hand, and regional parts, where
the part entities are the results of fiat delineation using arbitrary landmarks, as in Hand
has_regional_part Digit [76].
Aristotelian Definitions
A further crucial contribution of the FMA to the subsequent development of
biomedical ontologies is in the field of definitions. The goal of a dictionary
definition is to provide an explanation of the meaning of an expression that is
useful to humans. In the ideal case, the dictionary provides an explanation that
is built out of terms that are more familiar and simpler in meaning than the term
to be defined. Often, however, dictionary definitions will amount to mere
paraphrases, and they may be circular, either directly or indirectly (as when
term A is defined using term B, but term B is defined using term A). Often, too,
multiple, mutually inconsistent definitions are provided for a single term.
To reach the goal of providing a tool to support logical reasoning, FMA
requires a set of logically consistent definitions, with at most one definition for
each term and structured in such a way that each definition provides a
statement of individually necessary and jointly sufficient conditions for the
correct application of the term defined [77, 78]. To address these needs, Rosse
and his collaborators introduced the idea of what, drawing on the ideas of
Aristotle discussed above, they called “Aristotelian definitions.”
A definition of the form
S = def. a G which Ds,
where S stands for species, G for genus, and D for differerentia(e), tells us that,
if we know that something is a G which Ds, then we know that it is an S, and if
we know that something is an S, then we also know that it is a G which Ds. Here,
G is the immediate parent of S in the backbone taxonomy of the salient
ontology, and D is what sets apart those Gs which are Ss from the rest of the Gs.
An example from the FMA ontology is:
Anatomical structure [S] = def. Material anatomical entity [G] which ⌈is generated by
coordinated expression of the organism’s own genes that guide its morphogenesis; has
inherent 3D shape; is such that its parts are connected and spatially related to one another
in patterns determined by coordinated gene expression⌉ [D] [69]
where ⌈ ⌉ marks out the collection of sufficient conditions that forms the salient
specific difference. Together, G and D specify the essential characteristics
of any S. And a “group of entities that share the same set of essential
characteristics constitutes a class of the ontology” [79].
The Gene Ontology (GO)
Background
In 1977, Frederick Sanger and his collaborators sequenced the first full
genome, that of a virus called phiX174. Since that point, the biological and
biomedical sciences have been subjected to a process of upheaval as a result of
the need to take account of the gigantic amounts of molecular assay data that
have been generated in the wake of the successful completion of the human and
the various fly, mouse, fish, yeast, and other model organism genome projects.
Practically, all aspects of what we might call “old biology” were destined to be
transformed as biologists and clinical scientists worked out how to take
account of these new data in dealing scientifically not only with the many new
kinds of entity being disclosed at the molecular (and finer) levels through the
advance of science, but also with all the already recognized phenomena at
coarser levels of granularity (cell, tissue, organ, organism, population) upon
which biology and medicine had hitherto been based [80].
But how to make the gigantic quantities of new data discoverable and usable
by biologists in a situation where the primary source data live in many
independently developed biological databases? How to transform these many
efforts into a single cooperative force? Among the very earliest repositories for
the new data, created already in the 1970s, were the first protein structure
database (Protein Data Bank, https://www.rcsb.org/pages/pdb50/) and the
first mammalian genetics database (created at the Mouse Genome Informatics
(MGI) resource of the Jackson Lab, http://www.informatics.jax.org/). These
were followed in 1981 by the first depository for nucleotide sequences,
established in 1981 at the European Molecular Biology Laboratory (EMBL) in
Heidelberg (https://www.embl.org/about/history/). Each of these contributed
to the strategy of using molecular assay data deriving from model organisms to
advance our understanding of human health and disease, the idea being that
clinical scientists could harvest the results of experiments carried out on model
organisms in order to draw conclusions relevant to humans by exploiting crossspecies homologies. The reason gene (and corresponding protein) sequences
are similar between organisms is because of their descent from a common
ancestor. When GO was founded, it was widely hypothesized (and is now
supported by a great deal of evidence) that function is also generally conserved,
so that an experiment that elucidates an aspect of the function of a gene in the
mouse, or in yeast, could tell us about the function of related genes in humans.
The GO made it possible to test this hypothesis computationally at large scale
and, more importantly, to infer the functions of human genes by studying other,
more experimentally tractable systems. It is this idea which provided initial
impetus for the development of the GO.
By the turn of the millennium, the number of biological databases was
reaching a level where it had become unmanageable. Attempts to create a
federated system failed, not least because it was so hard to get the many groups
involved to agree on how the data should be structured and labeled. The fear, too,
was that such a federated system would create what Suzanna Lewis refers to as
“a technological behemoth that would be unable to respond to new
requirements when they inevitably occurred.”
The most fundamental questions for the biologists served by the model organism
databases revolved around the genes. … One essential aspect of this, which everyone
agreed was necessary, was systematically recording the molecular functions and biological
roles of every gene. ([81], emphasis added)
The Origins of the GO
In the 1990s, Michael Ashburner began assembling classifications of molecular
functions and biological processes, originally to serve the requirements of
FlyBase, the database for Drosophila genetics and molecular biology. At around
this time, different model organism communities began to see that they could
solve a significant portion of their data integration issues if a functional
classification system were created that was cross-species in nature. The goal
was to get the developers of databases focused on sequence (nucleic acid or
protein) together with the developers of other specialty biological databases
built for different ranges of organisms to agree on how this should be done in a
way that would work for all organism communities.
Lewis describes against this background how the GO came into being in
1998:
In July of that year, Michael Ashburner presented a proposal at the Montreal International
conference on Intelligent Systems for Molecular Biology (ISMB) bio-ontologies workshop
to use a simple hierarchical controlled vocabulary; his proposal was dismissed by other
participants as naïve. But later, in the hotel bar, representatives of FlyBase [Ashburner],
SGD [the Saccharomyces (yeast) Genome Database] (Steve Chervitz), and MGI (Judith
Blake) embraced the proposal and agreed jointly to apply the same vocabulary to
describe the molecular functions and biological roles for every gene in our respective
databases. Thus we founded the Gene Ontology Consortium. ([81]; compare [82, 83])
Note that the vision was not to create a database covering all functions of all
genes in all organisms. Rather—and here lay the brilliant insight of Ashburner,
Lewis, and their collaborators—it was to create a controlled vocabulary for
representing types of molecular functions and to use this vocabulary to annotate
(or “tag”) occurrences of references to corresponding genes or gene products in
literature or in data in such a way as to make the latter discoverable by third
parties from different branches of biology.
The GO became, in effect, an engine for searching in literature and data what
was still mostly hidden to outsider communities because it was inadequately
or inconsistently described. It was based on annotations created by human
beings (PhD biologists), pioneers in the new discipline of biocuration. The GO
itself was to a large degree populated through the work of such biocurators.
The annotations themselves would then be compiled, in conjunction with the
UniProt protein sequence repository [84], to form the GO Annotation database
(GOA) [85]. Then came more sophisticated software tools such as the Amigo
browser (http://amigo. geneontology.org/), which allowed a significant
fraction of the world’s biological literature and data to be subjected to filtered
search, allowing an investigator, for example studying the process of muscle
development in Bos taurus (cow), to find immediately all proteins documented
as involved in this process, all the articles in which this involvement is
documented, and the source and nature of the evidence which each of these
articles provides.21
21
http://amigo.geneontology.org/amigo/search/annotation?q=muscle%20development
The result was called “Gene Ontology”, not because it was an upshot of the
work on ontologies growing out of the KR and other computer-associated
disciplines in the preceding years, but merely because “ontology” was, in 1998,
the word du jour. The KR ontologies were in many cases, as we saw, products
of a view to the effect that for every different project a new ontology is needed.
The more ontologies, after all, the better. But then the results, for all their bangs
and whistles, proved (not surprisingly) useless as soon as their authors moved
on to the next project. The GO, in contrast, resulted from the insight that a
simple controlled vocabulary could unite the many sequence data-driven
projects springing forth on all sides. It started out not as a sophisticated
computer artifact, but rather as just a simple directed acyclical graph that could
serve as the basis for an indefinitely extendable project of annotation. The
nodes of the graph are terms22 (again: nouns and noun phrases, albeit now
associated with alphanumeric identifiers, definitions, URIs, and so forth), and
its edges are relations (initially just is_a and part_of).23
The fact that the GO was developed and maintained primarily by experts in
molecular biology led initially to a certain animosity between the GO
community and the community of those who had been developing ontologies
on the basis of their computer expertise. However, with the eventual adoption
by the GO community of OWL as their ontology development language, and
with the ever-increasing numbers of powerful software tools and algorithms
and research methodologies made possible by the existence of the GO and its
sister ontologies, this animosity has now largely disappeared.
Initially, too, there were sceptics on the biology side, above all Sydney
Brenner, winner of the 2002 Nobel Prize for his discoveries concerning
programmed cell death. In the same year, Brenner published a paper entitled
“Life sentences: Ontology recapitulates philology,” charging the GO Consortium
with the desire to transform genomics into what he called “genamics.”
To do serious theoretical work, Brenner held,
the network we should be interested in is not the network of names but the network of
the objects themselves. The language of these objects is not the Oxford Dictionary of
Molecular Biology … but the language of molecular biology itself. [There the] objects have
their own names: they are chemical names written in the language of DNA sequences and
the arrangements of amino acids on protein surfaces. [87]
What Brenner failed to see was that, even if all of us become fluent in the
language of chemical names, we would still need to connect what we can say in
this language with what we need to say in all the languages of old biology,
including, not least, the languages of clinical medicine.
Stefan Schulz (personal communication) points out that “label” is in some ways preferable
to “term.” A text string such as “Primary malignant neoplasm of lung (disorder),” for example,
would never be used by any human author of scientific text. In the end, however, he favors
over “term” the expression “representational unit,” whose advantages are outlined by Smith
et al. [86].
23
For the current set of relations in GO, see http://geneontology.org/docs/ontologyrelations/
22
Since its inception, indeed, the GO has gone from strength to strength. It is
today by far the world’s most successful scientific ontology, whether measured
along the dimensions of number and variety of associated software
applications; quantities of data and literature annotated using its terms;
number, size, and degree of utilization of major databases incorporating these
terms; numbers of experiments performed with its aid; and so forth.
There are multiple drivers of this success. One of the main ones is that the
GO and the GO annotations are hand built by human curators, who use the
scientific literature as a basis for their work. The result is an extract of
biological knowledge captured using GO (and sister ontology) terms and
relations, which has proved itself to be of tremendous utility.
There have, to be sure, been a number of proposals to leave population of the
GO to machine learning. The problem with this approach is that it is not possible
to create an algorithm that can extract knowledge from scientific literature
automatically [88]. Algorithms can be used for the sort of approximative text
translation that is made available by Google Translate, but they cannot achieve
results with the sort of accuracy that is required for the scientific purposes of
the GO [88].
One very fruitful application of GO is to what is called the enrichment
analysis of gene (product) datasets. In intervention studies (for example
genetic or pharmacological interventions) or time-series analyses, the GO can
be used to obtain an overview of the cellular locations, functions, and biological
processes in which the gene products are involved in order to develop
hypotheses about dependent variables or outcomes analyzed in such
experiments. The GO can also be used to classify and assess the status of
independent variables in order to identify confounding effects (hidden covariables). Powerful software applications have been developed for these
purposes, including the GO-Figure! visualization tool developed by Reijnders
and Waterhouse [89].24
Another reason for the GO’s success is that it makes certain sorts of
investigations possible that would just not be possible without it. The point is
not just that genomic data is annotated with the same shared ontology, nor that
this enables such data to be exchanged and integrated. Still more important is
that the resulting huge and ever-growing unified knowledge base about the
functions of genes makes it possible to interpret large-scale measurements of
gene expressions (or other -omics measurements) in relation to an unending
series of biological phenomena. Examples of studies, selected at random from
those published just in recent weeks, use the GO to identify pathways
implicated in suicide behavior, breast cancer survival, autism spectrum
disorders, involvement of calcium signaling in schizophrenia, association
signals of dental caries, disease modeling in C. elegans, and many more (Fig.
5.6).
24
At the same time, care must be taken to avoid misuse of the GO annotation data, for example
by failing to take account of the ontological structure of the GO itself or by ignoring the
evidence codes, which provide information as to the methods by which the data expressed in
annotations were obtained [90].
The GO Table of Categories
The three questions you want the answers to when you discover a new gene
product or complex are the following:
What does it do at the molecular level of granularity?
To what downstream biological processes does it contribute?
Where is it located in the cell?
The GO is accordingly divided into three sub-ontologies, whose respective
root nodes—referred to in the original GO paper [92] as the “Three categories
of GO”— are defined as follows:
Molecular function = def. Biochemical activity (including specific binding to ligands or
structures) of a gene product. This definition also applies to the capability that a gene
product (or gene product complex) carries as a potential. Examples: ‘enzyme’,
‘transporter’, ‘ligand’.
Fig. 5.6 Fragment from the GO Biological Process Ontology, from [91]
Biological process = def. Biological objective to which the gene or gene product
contributes. A process is accomplished via one or more ordered assemblies of molecular
functions. Processes often involve a chemical or physical transformation, in the sense that
something goes into a process and something different comes out of it. Examples: ‘cell
growth and maintenance’, ‘signal transduction’.
Cellular component = def. Place in the cell where a gene product is active. Examples:
‘ribosome’, ‘nuclear membrane’, ‘Golgi apparatus’.
Function in the GO
The GO has retained its original modular architecture and its general structure
and methodology over its more than 20-year history. But it has been subject
throughout this entire period to considerable revisions at lower levels. This is
primarily a matter of the depreciation of terms deemed obsolete, revision of
definitions, or addition of new terms and even of new families of terms, for
example covering hitherto underrepresented domains, such as immunology
[93].
The passage of time has seen also revisions to the original definitions of the
three GO categories, and it is especially in connection with the GO’s definition
of “function” that controversy has arisen. The current definition of molecular
function reads as follows:
10. Molecular function = def. Molecular process that can be carried out by the
action of a single macromolecular machine, usually via direct physical
interactions with other molecular entities. Function in this sense denotes
an action, or activity, that a gene product (or a complex) performs.
(http://geneontology.org/, as of August 7, 2021)
At the start, the assumption was built into the GO worldview that is_a
relations can never span the boundaries between the three GO sub-ontologies.
The functions at the (chemical) level of granularity of molecules in the GO thus
stand in some way opposed both to the processes occurring at higher
(“biological”) levels of granularity and to the locations in the cell.
Quite rightly, I believe, many of those who first encounter the GO are
therefore confused by the fact that its molecular function ontology is populated
primarily with terms designating types of activities—terms such as “ion
channel regulator activity,” “regulation of lysozyme activity,” “ceramide
floppase activity,” and “regulation of phosphatidate phosphatase activity.”
The new definition of molecular function (10) tells us, indeed, that
molecular function is_a molecular process. In normal usage, however, and also in
the usage of many philosophers, functions are not a special type of process.
Rather, they are certain sorts of historically grounded potentials or capabilities
in things that can be realized in processes when suitable circumstances obtain.
As the term “function” is normally understood, a function can fail entirely to be
realized, or it can be misrealized. A well-oiled machine, for example, will indeed
perform its function in normal circumstances, but when things go wrong, then
it can behave (act) in all sorts of nonfunctional—or, as we might also say,
noncanonical—ways.
In a sense, the functions of the macromolecular machines inside an organism
are being continuously realized, just as the principal function of the organism’s
heart is being continuously realized for so long as the organism is alive. But
macromolecular machines change their activity patterns. For example, the
sleeping brain is biochemically very active, but the pattern of activity differs
from the wake pattern, and it differs again if one suppresses the normal rest
pattern by taking sleeping pills or alcohol or both. Strictly speaking, of course,
these latter cases are irrelevant to the GO. The GO, too, is a canonical ontology,
and the scope of its molecular function ontology is determined by those
molecular level processes that the organism evolved to perform because it
allowed the organism to better survive and reproduce. This is the meaning of
“canonical” for molecular (and indeed for all) functions.
The GO is canonical also in that it does not deal, for example, with processes
which are induced experimentally. Unrealized functions at the molecular level
are also out of scope. However, the cyclic nature of many activities in
organisms means that the referent of “activity” will even in many canonical
cases differ from one phase to the next. The referent of “function,” in contrast,
will always be the same.
Defining precisely the meaning of the term “function” is a nontrivial matter,
and philosophers and others have proposed various alternative definitions. In
the Gene Ontology Handbook, Paul Thomas provides an account of the GO’s
usage of “function” according to which it is the standard etiological or “selected
effect” definition of function that is intended by the GO [94]. We believe that his
arguments for this interpretation—an interpretation which we also defend
[95]—are sound. Unfortunately, as we shall see, the strength of his arguments is
diminished by the fact that he adopts the terminological convention at work
already in the original GO definition of “molecular function” provided in the
previous section of this chapter. He neglects an important distinction, namely
that between a function (program, capability), on the one hand, and its
corresponding activity (realization/execution/performance) on the other.
In a simplified version of the selected effect account (based on Millikan [96],
which Thomas also cites), a function is defined as follows:
11. A has function F = def. A originated as a reproduction (for instance as
offspring, or as copy) of some prior item or items that, due in part to
possession of the properties reproduced, have actually performed F in the
past, and A exists because (causally historically because) of this or these
performances.
It is, very briefly, the function of my heart to pump blood because my
ancestors’ hearts’ pumping blood through their bodies kept them alive and
because I exist because of this. We note that, according to this definition, it
would still be the function of my heart to pump blood even if (for example
because I am connected to a heart-lung machine) it is currently unable to do
so. It would still be the function of my screen to display pixels even if (for
example because my machine is switched off) it is currently unable to do so.
As Thomas correctly points out, it is an advantage of the selected effect
approach that it explicitly incorporates evolutionary considerations by
requiring that the function of any biological entity ultimately derives from its
history of natural selection. The approach thereby provides a method for
determining which—among the myriad potential alternative effects the
actions of a particular entity might have—are properly to be considered as the
exercise of its function. One effect of my heart pumping, for example, is to
produce sound, but this is not a part of the function of my heart, because this
effect was not selected for.
The terminology of the GO has been built in such a way as to do justice to
the selected effect account of function, but in a way that most subtypes of
“function” are labeled “activity.” This is not because Thomas and others failed
to appreciate the difference between function and activity, but rather
because, in the canonical world of the GO, function and activity go so tightly
hand-in-hand with each other that it would be terminologically redundant to
provide representations of both (thus both to catalyze and catalytic activity,
both to regulate and regulating activity, and so forth—compare also the
function and process columns in Table 5.1). The simplest would be to rename
the GO “molecular function” ontology, and to call it instead the “molecular
activity ontology” or—following a practice which I understand is already
favored in certain GO circles—the “molecular functioning ontology,” thereby
adding the explanation that molecular “activity”/“functioning” means “the
exercise of a function of a macromolecular machine” and providing as part of
its glossary a suitable definition of “function.”
It is important to keep both “activity” and “function” in circulation, however.
For it might certainly be the case that, in the canonical world of the GO, it is
trivial that any activity of Xing that is realized under a particular set of
conditions (which is in practice how evidence to assert that a gene product is
an instance of a given GO class is obtained) is also the realization of a function:
to X. But this is no longer true when GO is being used in those areas where
there are departures from what is canonical. There are multicellular systems
in my heart, which have the function to contract. This function remains one
and the same even under those noncanonical conditions, where my heart is
not functioning very well and where contraction activities therefore depart
from the canonical.
Thomas summarizes his account of molecular function in two places, as
follows:
12. In the GO, a molecular function is a process that can be carried out by the
action of a single macromolecular machine, via direct physical interactions
with other molecular entities. Function in this sense denotes an action, or
activity, that a gene product performs.
13. A function as conceived by molecular biologists (in what could be called the
‘molecular biology paradigm’) refers to specific, coordinated activities that
have the appearance of having been designed for a purpose. That apparent
purpose is their function.
To do justice to the ontological distinction between function and
processes/activities that realize them, these would need to be amended to read:
14. In the GO, a molecular function *is realized in* a process that can be carried
out by the action of a single macromolecular machine, via direct physical
interactions with other molecular entities. *This realization is* an action, or
activity, that a gene product performs.
15. A function as conceived by molecular biologists (in what could be called the
‘molecular biology paradigm’) *is what is involved where* specific,
coordinated activities have the appearance of having been designed for a
purpose. That apparent purpose is their function.
Extending the GO
Interestingly, in elucidating his account of function, Thomas draws on Jacques
Monod’s idea in Chance and Necessity [97] of teleonomy. Monod defines this as
“the characteristic of objects endowed with a purpose or project, which at the
same time they exhibit through their structure and carry out through their
performances” (p. 9). This applies to artifacts such as screwdrivers, which are
designed to have a certain purpose. For living systems, however, we cannot talk
of design. Rather, as Thomas writes, “what appears to be a future-goal-oriented
action by a living organism is, in fact, only a blind repetition of a genetic
program that evolved in the past.” More completely, however—for there are
two series of blind repetitions here—he should write:
1. The program is copied over and over again through (blind) biological
processes of copying the relevant entity (for instance the relevant
macromolecular machine).
2. The execution of each copy of the program is repeated in the successive
realizations of that entity’s function.
Teleonomy, for Monod, is present at all levels of a biological system, from
proteins (which he calls “the essential molecular agents of teleonomic
performance”) to “systems providing large scale coordination of the organism’s
performances … [such as] the endocrine and nervous systems” (op. cit., p. 62).
At all levels, indeed, we have objects, and systems and parts of objects,
performing (activities) which realize apparent purposes (functions), such as
pumping blood, regulating chemical levels in the blood, and removing
damaged cells from the blood. And in each of these cases, we have to deal not
only with functions of the body involving groups of cells interacting via
molecules or ions, but also with functions of parts of the body at higher levels
of granularity than molecules, which we might therefore call biological
functions. And interestingly, although Thomas’s [98] paper deals almost
exclusively with functions at the molecular level, its title is “The Gene Ontology
and the Meaning of Biological Function,” though by this he means not the
functions of organs such as heart or lungs, but rather of systems of
macromolecular machines, which Monod sees as analogues of cybernetic
systems, thereby reflecting the way in which biologists today conceptualize the
feedback loops constructed from multiple molecular activities.
Ontological categories
Function
Level of granularity
To pump blood
biological
program for heart
Molecular
system, cell,
organ,
organism
contraction: set of
molecular
Process
Pumping blood
GO:0060047: heart
contraction —
multicellular
organismal process in
functions that, if
which the heart
executed in the
proper context,
decreases in
volume in a
would result in
characteristic way
heart contraction
Object
Heart
to propel blood
through the body
Molecule
To catalyze a
biochemical
reaction
Catalysis of a
chemical
reaction
Catalyst
Table 5.1 In this table the GO ontology is extended by modules to accommodate biological
functions, and their bearers, at higher levels of granularity. The table is adapted from the
presentation described in footnote 27, but incorporating amendments by Paul Thomas
(personal communication)a
Table 5.1 depicts, on this basis, the GO architecture extended in such a way
that an explicit division is drawn between levels of granularity along the
vertical axis and kinds of entity along the horizontal, where the shaded cells
correspond (roughly) to the coverage domain of the original GO.
Thomas now describes his own position as follows (personal communication): ‘of course for
GO, it is all ultimately at the level of molecules. It is the Gene Ontology—it is a
conceptualization of how genes (technically, gene products, which are molecule types that
are encoded by genes) function at the molecular level and at the system level. Essentially, the
system level for molecular biologists is conceptualized as a highly integrated, coordinated
execution of individual molecular activities. So in GO, the system level (biological process) is
also represented in terms of gene products and their activities/functions. GO was not
constructed for describing the functions of higher order objects like the heart, though of
course in practice, it is natural to describe some biological programs in terms of higher order
objects. For example, GO describes the genetic programs (BP), carried out by the activities of
gene products (MF), that result in heart contraction. GO also describes the genetic programs,
carried out by the activities of gene products, that result in the construction of the higher
order objects themselves (e.g., heart development). But GO biological processes also include
subcellular processes: genetic programs that transmit a message (in the form of molecules of
a given type) from outside a single cell to the cell nucleus (e.g., the Wnt signaling pathway),
which is executed only by a set of molecule types, not a higher order object.’
a
The Open Biological and Biomedical Ontologies (OBO)
Foundry
The Birth of OBO
As we learn from the subtitle of the landmark paper [92], the GO was originally
conceived as a “tool for the unification of biology.” In other words, the GO was
built to foster the melding together of biological datasets and techniques
across disciplines, across levels of granularity in the organism, across species,
and across geographically dispersed communities of originators and compilers
of data and of researchers using these data. An example of success in this
regard is the way in which the GO enables communication across all the
disciplines collaborating for example in a field such as aging research, which
involves the study of model systems of human aging in organisms as diverse as
yeast, reptiles, and whales.
Already in 2001, the trail laid by the GO opened the way for the creation of
a series of controlled, cross-species vocabularies for neighboring areas of
biology through the creation of a public ontology repository, originally (we
imagine for a very short time) referred to as “GOBO,” for “Global Open
Biomedical Ontologies” [99], and subsequently dubbed the “OBO Library.”
The rules for building ontologies for this library can be found in a tutorial
presented by Ashburner and Lewis at the Intelligent Systems for Molecular
Biology (ISMB) conference in 2005 on “Principles of Biomedical Ontology
Construction” (http://bit.ly/2GUkpoh) [100]. Most important are that the
ontology must be shared without limit, and thus that it must be in the public
domain and easily findable;25 that it is used in application to actual instances of
important scientific data; and that it is maintained in such a way that, where
such application leads to identification of errors and gaps, the latter will be
promptly rectified.
In the case of the GO, this strategy produced a positive snowball effect,
making the GO increasingly attractive to successive cohorts of new users, who
themselves identify new errors and gaps, giving rise to a regimen of continual
improvement of a sort that was unknown to ontologies before the GO.
The Birth of the OBO Foundry
The OBO Foundry was first conceived at a meeting held in Leipzig in 2004 on
the topic of The Formal Architecture of the GO. Other groups from the KR and
OWL communities had attempted earlier to interest the GO community in the
benefits of a more ambitious approach to ontology development, especially as
concerns the treatment of logic and definitions. Where these earlier efforts had
failed,26 some of the new arguments presented at this meeting drawing on the
perspective of ontological realism met with greater success.27
25
The OBO community here anticipates the modern FAIR approach [101].
No less important was the introduction of new rules to promote coordinated
ontology development, the idea being that ontologies would be admitted as
members of the Foundry initiative only if their developers had committed in
advance to certain principles, for example relating to working within set
boundaries (for example of proteins or cell types) and agreeing to collaborate
on those terms which relate to entities in areas where boundaries overlap.
The details of Foundry organization were then worked out in a series of
meetings, some of them under the auspices of the then newly established
National Center for Biomedical Ontology (NCBO).28 Building the Foundry was
viewed as amounting to distinguishing within the original OBO Library as a
whole an inner compartment comprising, at any given stage, those ontologies
certified to have satisfied both the Library principles and also a series of
additional principles specific to the Foundry, designed to advance the quality
and interoperability of its included ontologies [103].
The core goal of the OBO Foundry—where “OBO” is now understood as
meaning “open biological and biomedical ontologies”—like that of the OBO Library
is to create a situation where ontologies would support efficient knowledge
accumulation in the life sciences by providing recommended sets of terms for
annotating data in each life science domain—thus one set of terms for proteins
[104], one set of terms for small molecules (or chemical entities of biological
interest: [105]), one for plants [106], and so forth. The terms in each ontology
would be accompanied not only by natural language definitions designed to
ensure that the terms are correctly used by those (humans) involved in creating
annotations of biological literature and data, but also by formal definitions designed
to promote computer-aided reasoning with the resulting annotated data. For the
Foundry ontologies, a layer of governance was introduced—in some ways
analogous to the editorial board of a scientific journal—and a process of
review was established which would certify conformance to the Foundry
principles. The current set of principles includes the requirement to use a
standard ontology language (currently OWL) and use of Basic Formal Ontology
(see below) as shared top level. In fact, BFO [64] makes itself manifest already
in the terminology used in the top two rows of Table 5.2, which depicts the
initial structure proposed for the OBO Foundry, a structure which in effect
extends to include also ontologies external to the GO.
One member of the OWL community remarked to me at the time that “a meeting on the
formal architecture of the GO? Well … that would have to be a very short meeting, then.”
27
These arguments were summarized in a presentation by the author entitled “STOP!” (for
Smart Terminologies through Ontological Principles—http://ontology.buffalo.edu/04/STOP_GO_5_04. ppt), whose goal was to show how the realist perspective can help in the
identification of errors in the GO. See also [75, 102].
26
In a parallel development, there arose at about the same time what would
become a much larger biomedical ontology repository extending the original
OBO
Library
idea,
namely
the
NCBO
BioPortal
(https://bioportal.bioontology.org/), which provided the advantage of providing
access to ontology-structured versions of SNOMED CT, HL7, MeSH, and other
major resources from the world of medical terminology. The BioPortal adopted
a very liberal strategy of acceptance of ontologies, which was in a sense at the
opposite extreme from the strategy of the OBO Foundry.29 This, however,
created for the BioPortal a problem of redundancy and lack of consistency
between the (now of the order of) 500 ontologies listed, a problem which was
further exacerbated as new ontologies were developed incorporating reuse of
terms and definitions from multiple already established ontologies but
supplying them with new term identifiers and new URIs. This conflicts with the
OBO Foundry goal of creating a set of mutually consistent and non-redundant
ontologies for the life sciences that would promote for each term a unique
recommended natural language definition, formal definition, and URI.30
Basic Formal Ontology (BFO)
BFO was adopted as required top level for ontologies in the Foundry in order
to make available a common set of categories (highest level universals) that
would serve as the shared starting point for the definitions of lower level
universals included in the coverage domains of the separate biomedical
ontologies in the Foundry.
BFO itself was developed as a very small representational artifact with the
narrowly focused task of providing a top-level ontology, which could be used
to support the integration of domain ontologies developed for purposes of
scientific research. As Tables 5.1 and 5.2 make clear, the structure of the set of
ontology modules of the OBO Foundry is derived, in effect, by taking the crossproduct of BFO’s top-level categories with the multiple granular levels (of
molecule, cell, organ, organism, population) relevant to biology. The FMA was
the first extensively populated ontology to take advantage of the theoretical
foundations of such a toplevel ontology and thereby extend the latter into the
biomedical domain [72, 108].
28
http://ncorwiki.buffalo.edu/index.php/NCBO_Sponsored_Dissemination_and_Training_
Events_2005-2015
29
Later, there arose the Ontobee portal (http://www.ontobee.org/) [107], a biomedical
ontology repository that is optimized for the purposes of the OBO Foundry using the
technology of a linked data server. Ontobee is a linked ontology data server, supporting
ontology term dereferencing, linkage, query, analysis, and integration.
30
The most recent versions of the BioPortal go some way to solving this problem by
generating search results in such a way that the original source definition of a term is
returned first in the list of all the ontologies, which use that term with that definition.
Relation to
time
Continuant
Independent
Occurrent
Dependent
Granularity
Organ and
organism
Cell and cellular
component
Molecule
Organism
(NCBI
Taxonomy)
Cell (CL)
Anatomical
Entity
(FMA,
CARO)
Cellular
Component
(FMA, GO)
Molecule (ChEBI,
SO, RNAO, PRO)
Organ
Function
Cellular
Function
(GO)
Phenotypic
Quality
(PaTO)
Molecular Function
(GO)
Biological
Process (GO)
Molecular Process
(GO)
Table 5.2 The projected structure of the OBO Foundry from around 2005 (shaded
regions correspond to the three original GO ontologies)
Adopting BFO allowed:
1. The explicit formulation of aspects of the development methodology and
architectural structure of the OBO Foundry (and other) ontologies in ways
that have helped steer their subsequent development [64]
2. Providing a readily applicable technique for formulating definitions of
terms in these ontologies [78]
3. Formalizing relations [73]
4. Supporting the strategy of cross-product definitions [109], whereby
definitions in one OBO Foundry ontology will draw on terms defined
already in other such ontologies, for example in the case of those GO terms
whose definitions incorporate representations of molecules drawn from the
ChEBI chemistry ontology, as described by Hill et al. [110]
5. Formally encoding the OBO Foundry principles as operational rules and
applying the resultant checks across the full OBO suite of ontologies, thereby
demonstrating how a sizable federated community can be organized and
evaluated on objective criteria that help improve overall quality,
interoperability, and sustainability [111]
The Evolution of BFO
There have been four releases of BFO thus far.31 Version 1 was released in 2001,
and the influence of Aristotle’s table of categories on this first version can be
seen in the similarity of terminology and structure as between the upper rows
of Fig. 5.2 and those of Fig. 5.7. Another influence was the top-level ontology
DOLCE [112]. BFO shared with DOLCE from the very start an architecture
based on two orthogonal divisions of entities into disjoint categories of (1)
continuant vs. occurrent and (2) independent vs. dependent. Material objects
(Aristotle’s substances) are independent continuants; qualities are dependent
continuants; and processes are occurrents. Entities in all of these categories
exist on the level of both universals and instances.32 The release of BFO 1.1 in
2007 was prompted by the need to enable coverage of information artifacts,
nucleotide sequences, and similar (copyable) entities, a need which arose
with the birth of two new ontologies, the Ontology for Biomedical
Investigations (OBI) in 2006 [62, 115]33 and the Information Artifact Ontology
(IAO; see [60]), which provided terms used to represent entities such as
publications, footnotes, protocols, and databases.
The release of BFO 2 in 2015 reflects the transition from an OWL DL to an
OWL 2 formalization, as well as the addition of term IDs and of temporalized
relations. A preliminary version was released for review at the 2012 meeting
of the International Conference on Biomedical Ontology.
By the year 2020, BFO has come to serve as something of a stable attractor
to ontology developers (http://basic-formal-ontology.org/users.html, [117]),
thereby giving rise to powerful network effects analogous to those brought by
the QWERTY keyboard and the TCP/IP internet protocol, whereby each
successive new user BFO raises the value of the artifacts created on its basis by
earlier users, in another positive feedback loop.
As a consequence of these developments, the Joint Technical Committee on
Information Technology (JTC 1) of the International Standards Organization
(ISO) and the International Electrotechnical Commission (IEC) have approved
in 2021 the ISO/IEC 21838: Top-Level Ontologies (TLO) standard. Part 1 of this
standard sets forth the requirements for being a top-level ontology. Part 2
documents BFO in a way that demonstrates satisfaction of these requirements.
The release of BFO-2020 includes a more careful treatment of definitions.
All non-primitive terms have been provided with English language definitions
(which means statements of individually necessary and jointly sufficient
conditions). All primitive terms have been provided with elucidations, which
means statements of necessary conditions together with specifications of
examples of use. Additional improvements concern the logical formalization of
BFO. Along with an OWL version of BFO-2020, the ISO standard provides also
an axiomatization in common logic (BFO-2020-CL) and a translation thereof
into FOL. A proof of consistency of BFO-2020-CL is provided, together with a
proof that BFO-2020-OWL is derivable therefrom. English language definitions
31
Successive versions are available through http://ontology.buffalo.edu/bfo
A further influence was lessons learned from work on a framework that would link the
quantitative data studied in the new field of Geographic Information Science with qualitative
data pertaining to the hills and valleys, and rivers and lakes, that form the subject matter of
what we might call old geography ([113]; compare also [114]).
33
OBI is now an OBO Foundry ontology. It was created as a generalization of the Functional
Genomics Investigation Ontology (FuGO) [116].
32
Fig. 5.7 BFO-2020 is_a hierarchy from ISO/IEC 21838-2 (https://www.iso.org/standard/74572. html)
and elucidations provided in the standard are formulated in such a way as to
be as close as possible to BFO-2020-CL while at the same time serving as an
access route to the content of BFO-2020 for human users.
Conclusion
Like the Gene Ontology, and like the Planteome ontologies, which are seen by
their developers as “integrative tools for plant science” [118], the OBO Foundry
as a whole is a tool for the unification of biology. Indeed, all the OBO Foundry
ontologies continue in their way the project of the Vienna circle to achieve the
unification of science. They do this, however, not from the starting point of logic
and philosophy, but rather from the starting point of biology and ontology. And
they do this more successfully, because their project of unification is deeply
interwoven, through multiple different sorts of multidirectional interactions,
with ongoing developments in biology and, increasingly, in clinical sciences.
Acknowledgments Thanks go to Werner Ceusters, Janna Hastings, Patrick Hayes, Yongqun
(Oliver) He, Jobst Landgrebe, Suzanna Lewis, Cornelius Rosse, Stephan Schulz, and Paul
Thomas. Work on this chapter was supported by the NIH/NCATS 1UL1TR001412 Buffalo
Clinical and Translational Research Center CTSA Award.
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Biomedical Ontologies
Peter L. Elkin (ed.), Terminology, Ontology and their Implementations, Cham, Switzerland: Springer Nature (2023), 2023
We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the nineteenth century and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the twentieth century. We show how aspects of this idea, particularly those relating to the use of the term “concept” in ontology development, influenced SNOMED CT and other medical terminologies. Against this background, we then show how the Foundational Model of Anatomy, the Gene Ontology, Basic Formal Ontology, and other OBO Foundry ontologies came into existence and discuss their role in the development of contemporary biomedical informatics....Read more
