Zeng marcia ifla-subjectaccesssmartdatadh

Subject Access, Smart Data, and
Digital Humanities
– Finding Unlimited Opportunities through
their Intersections
Marcia Lei Zeng
Kent State University
Keynote @ IFLA Classification & Indexing Satellite Conference
2016 August 11-12, Columbus, OH, USA
http://marciazeng.slis.kent.edu/

Outline
• I. Background
• II. Subject Access -- Finding the Unlimited
Opportunities
• III. The Importance of Knowledge
Organization Systems (KOS) for Effective
Subject Access
M Zeng - IFLA Classification & Indexing
Satellite Conference 2016
28/11/16

I. Background
What do I mean…
1) Subject access
– in the context of today’s environments
2) Smart data
– in the context of Big Data
3) Digital Humanities
– in the context of heritage institutions’ data
38/11/16

What is happening around us?
• The 2nd generation of the Web:
the Semantic Web
– Search engines involvement,
– mature of the Linked Data technologies,
– non-traditional databases
• “Big Data”
– Government funding opportunities,
– Blooming of ‘data analytics’ profession
• Modern AI (artificial intelligence)
– Machine-learning
– Contextual computing
• Participatory culture
– Social media
– Engaging end-users in the workflow
M Zeng - IFLA Classification & Indexing Satellite Conference 2016 4
I. Background
https://www.w3.org/2002/Talks/www2002-w3ct-swintro-em/slide7-0.html
8/11/16

Source: Nova Spivak, Radar Networks; John Breslin, DERI; & Mills Davis, Project10X,
2007, 2008 Copyright MILLS•DAVIS
5
Web 1.0: connecting information and getting on the net.
Web 2.0: connecting people — putting the “I” in user interface, and the “we” into Webs of social participation.
Web 3.0 Connecting knowledge -- representing meanings, connecting knowledge, and putting these to work in ways that make our
experience of internet more relevant, useful, and enjoyable.
Web 4.0 Connecting intelligence -- It is about connecting intelligences in a ubiquitous Web where both people and things reason and
communicate together.
8/11/16

6
Big data
• Volume (data quantity)
• Velocity (data speed)
• Variety (data types &
nature)
• Variability (data
consistency)
• Veracity (data quality)
• Complexity
Source: Kobielus, James. 2016. The Evolution of Big Data to Smart Data. Keynote at Smart Data Online 2016.
Source: Big Data. Wikipedia.
SAS Institute Inc. [2014]. Big Data:
What it is and why it matters.
Smart Data
= Ability to achieve big
insights from such data
at any scale, great or small.
I. Background
8/11/16

Why Smart Data
• “However, in its raw form, data is just like crude oil; it needs to be refined and
processed in order to generate real value. Data has to be cleaned, transformed, and
analyzed to unlock its hidden potential.” (TiECON East. Data is new oil.)
• Once tamed through organizing and integrating processes, large volumes of
unstructured, semi-structured, and structured data are turned into “smart data” that
reflect the research priorities of a particular discipline or field.
• Smart data inquiries can then be used to provide comprehensive analyses and
generate new products and services.
7
Sources:
Gardner, D, 2012.
Prithwis Mukerjee, 2014
Schöch, 2013.
TiECON East, 2014.
8/11/16

What can we do to avoid asthma episode?
8
Real-time health signals from personal level (e.g., Wheezometer, NO in breath,
accelerometer, microphone), public health (e.g., CDC, Hospital EMR), and population level
(e.g., pollen level, CO2) arriving continuously in fine grained samples potentially with missing
information and uneven sampling frequencies.
Variety Volume
VeracityVelocity
Value
What risk factors influence asthma control?
What is the contribution of each risk factor?
semantics
WHY Big Data to Smart Data: Asthma example
Slide from: Sheth, Amit. 2014. Transforming Big Data into Smart Data: Deriving Value via harnessing Volume, Variety and Velocity
using semantics and Semantic Web.
Understanding relationships between
health signals and asthma attacks
for providing actionable information
8/11/16

9
Data
(in humanities)
Big data
unstructured
messy
implicit
relatively large in
volume
varied in form
Smart data
semi- structured
or structured
Clean
Explicit and
enriched
Raw data
+ markup, annotations
and metadata
Relatively small in
volume
The creation involves
human agency & demands
time
The process of modeling
the data is essentialOf limited
heterogeneity
Complied based on Schöch, Christof. 2013. Big? Smart?
Clean? Messy? Data in the humanities. Journal for Digital
Humanities. 2(3)
What about LAMs?
8/11/16

Structured
Semi-structured
Unstructured
10
• National bibliographies
• Catalogs
• Special collection portals
• Registries
• Metadata for datasets
• …
• Text Encoding Initiative (TEI) files
• Finding Aids
• Value added/tagged resources
• Unstructured portion within metadata
descriptions
• Digitized materials, textual or
non-textual
• Original information-bearing
objects
• Documents in all kinds of formats
• …
• Data from Web crawling that
need to be cleaned
• … …
LAM data examples
“Smart Data” emphasizes the
organizing and integrating processes
from unstructured data to structured
and semi-structured data, to make the
big data smarter.
- Schöch, 2013
8/11/16

• the field is still expanding,
• the definitions are being debated, and
• the multifaceted landscape is yet to be fully
understood.
• Most agree that initiatives and activities in digital
humanities are at the intersection between the
humanities and digital information technology.
• The field applies big data mathematical research
techniques to the description and analysis of cultural
objects—including art, literature, and technological
artifacts themselves.
11
Image source: Katherine Hayles
http://dtc-wsuv.org/wp/dtc375-
scodi/katherine-hayles/
• Svensson, P. 2010. The Landscape of Digital Humanities. Digital
Humanities Quarterly. 4(1).
• Svensson, P. 2009. Humanities Computing as Digital Humanities.
Digital Humanities Quarterly. 3(3)
I. Background
8/11/16

Advanced technologies now allow researchers :
(under the umbrella of Big Data and the Semantic Web)
• to access and reuse large volumes of diverse data,
• to discover patterns and connections formerly hidden
from view,
• to reconstruct the past,
• to discover impacts in real and virtual environments, and
• to bring the complex intricacies of innovations to light,
all as never before.
12
Image source: http://goo.gl/a4gZsd
Image source: Schöch, 2013.
8/11/16

13
Think:
• What kind of data did the project use?
Data sources：
• Freebase (now Wikidata)
• Union List of Artist Names (ULAN®)
• Allgemeines Künstlerlexikon/ Artists of the World
Schich, M. et al. 2014. “A Network Framework of Cultural History.”
Science, 345(6196), 558-562.
Nature Video. (2014, July 31).
Charting culture.
https://www.youtube.com/w
atch?v=4gIhRkCcD4U
8/11/16

Advanced technologies now allow researchers :
(under the umbrella of Big Data and the Semantic Web)
• to access and reuse large volumes of diverse data,
• to discover patterns and connections formerly hidden
from view,
• to reconstruct the past,
• to discover impacts in real and virtual environments, and
• to bring the complex intricacies of innovations to light,
all as never before.
Data provided by LAMs and cultural heritage institutions are
treasures for all humanities researchers.
Trending:
• Machine readable understandable data
• Machine readable actionable data
• Accurate (no error) data in the processes of interlinking,
citing, transferring, rights-permission, use and reuse, etc.
• One –to -many uses and high efficiency processing data
14
http://goo.gl/a4gZsd
8/11/16

15
Digital humanities – Librarian Survey Results, December 2015
http://americanlibrariesmagazine.org/2016/01/04/special-report-digital-humanities-libraries/
Source: http://americanlibrariesmagazine.org/wp-content/uploads/2016/01/digital-humanities-faculty.pdf
8/11/16

16
Digital humanities – Faculty Survey Results, December 2015
Source: http://americanlibrariesmagazine.org/wp-content/uploads/2016/01/digital-humanities-faculty.pdf8/11/16

Structured
Semi-structured
Unstructured
17
• Catalogs
• Registries
• …
• Text Encoding Initiative (TEI files)
• Finding Aids
descriptions
non-textual
objects
• …
need to be cleaned
• … …
LAM data examples
II. Subject Access
-- Finding the Unlimited Opportunities
8/11/16

Figure. Overview of Relationships (draft)
Source: http://www.ifla.org/files/assets/cataloguing/frbr-lrm/frbr-lrm_20160225.pdf + revision draft
FRBR-Library Reference Model (LRM)
- World-wide review version
RES:“Any entity in the universe of discourse”
8/11/16
18

liked, cited, researched, tagged, searched,
shared, followed, time spent, …
Three Perspectives
-- from the creation of the structured data
based on Rose, Gillan. 2013.
Visual Methodologies, 3rd. Ed.
• Index
• Markup
• Ontology
• Knowledge
base
• Metadata
• Descriptive
• Administrative
• Structural/techni
cal
19
1 2
3
Production Content
Audiences’ receiving interests
Image sources:
http://www.mahalo.com/how-to-
understand-perspective-in-drawing/
http://judithlondono.com/
http://www.smrfoundation.org/nodexl/
8/11/16

20
Read more: Godby, Wang, Mixter. 2015. Library Linked Data in the Cloud –
OCLC’s Experiments with New Models of Resource Description. ISBN
9781627052191.
Figure 1.1: A bibliographic description as a record and a graph.
WorldCat Linked Data
https://www.oclc.org/developer/develop/linked-
data.en.html
OCLC WorldCat Works
– 197 Million Nuggets of Linked Data
-- Since 2014-
“The bibliographic metadata found
in WorldCat contains a rich set of
objects that can be represented in
linked data.”
Linking THINGS, not strings.
Access through the linked things.
8/11/16

21
BibFrame
- based
Read: https://www.denverlibrary.org/blog/rachel-f/dpl-announces-linked-data-launch 2015-06
Try: http://labs.libhub.org/denverpl/
8/11/16

22
http://www.worldcat.org/oclc/922220005
http://worldcat.org/entity/work/id/2534768
http://worldcat.org/entity/person/id/2631227899
Linking out
Schema.org- based
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23
Go to http://dbpedia.org/page/Lois_Mai_Chan and
follow knownFor and about
8/11/16

24
The connected structured data for THINGs from
Perspectives #2 and #3:
2
3
8/11/16

Structured
Semi-structured
Unstructured
25
• Catalogs
• Registries
• …
• Text Encoding Initiative (TEI files)
• Finding Aids
descriptions
non-textual
objects
• …
need to be cleaned
• … …
LAM data examples
There are many hidden access
points that can bring in much
richer information and
knowledge through LAM data.
8/11/16

Big text in humanities
“Big text”
– The text version of “big data”
– Where?
• special collections,
• archives,
• oral histories,
• annual reports,
• provenance indexes,
• inventories,
• … etc.
– How?
• Fact mining, analytics
– What is needed?
Tools
• to ‘mine’ the text,
• to manage extracted
entities as new access
points, and
• to connect with the
outside data.
26
Source: SmarkLogic webinar
http://www.marklogic.com/w
ebinars/
8/11/16

• Index
• Markup
• Ontology
• Knowledge
base
• Metadata
• Descriptive
• Administrative
cal
27
1 2
3
Production Content
2). Taking archival finding aids as an example
Image source:
http://alelemuseum.tripod.com/Archive
s.html
Image source:
https://libraries.u
sc.edu/article/ins
ide-usc-libraries-
grand-avenue-
library
8/11/16

• Finding aids
• Provide detailed descriptions of a collection's component parts,
• summarize the overall scope of the content,
• convey details about the individuals and organizations involved,
• list box and folder headings.
• The ‘subject’ access is to the whole archive (=Perspective #1)
• Few provided accesses to the ‘things’ contained in the contents
through index terms.
[Images from a finding aids: title page, content page, and index terms page.
Image source:
https://libraries.u
sc.edu/article/ins
ide-usc-libraries-
grand-avenue-
library8/11/16
28

Finding Aids
semantic analysis
using ontology-based tools
by KSU SLIS LOD-LAM team
http://lod-
lam.slis.kent.edu/SemanticAnalysis.html
• 45 archival finding aids
• drawn from 16 repositories
• From OpenCalais: extracted 8,096 entities and 336
suggested social tags
29
OpenCalais and COGITO are
• semantic analysis/fact-mining tools,
• taxonomy and ontology-supported,
• with machine learning and natural language processing
behind.8/11/16

after
before
30
http://www.opencalais.com/
8/11/16

Structured data
produced by Calais
(RDF/XML)
31
http://www.opencalais.com/
8/11/16

before
after
32
http://www.intelligenceapi.com
8/11/16

after
after
Structured
data for
THINGs
33
before
http://www.intelligenceapi.com
8/11/16

34
for enhancing access to the contents in oral history transcripts files.
• Currently many are managed at collection level only.
• Only some have deep indexes, with great quality.
• The indexes usually existed as a ‘back-of-the-book’ style
and stayed within PDF files, downloadable.
• The indexes could be used in the
collection's subject searching.
• Indexed THINGs could be linked to
external resources.
The same approach can be used for the oral history collections
[image of a back-of-the-book style index
to an oral history transcripts]
[image of a page of the oral history
transcripts]
8/11/16

35
Tool used: Open Calais
Note: Only for assistant extraction; still need
human cleaning process.
The same approach can be used for the library catalogs
8/11/16

36
The same approach can be used for the museum object labels and descriptions
COGITOOpenCalais
http://www.metmuseum.org/toah/works-of-art/2010.312/
8/11/16

AfterBosonBefore http://bosonnlp.com/demo
entities
keywords
8/11/16
37

• Index
• Markup
• Ontology
• Knowledge
base
• Metadata
• Descriptive
• Administrative
cal
38
1 2
3
Production Content
3) Taking non-textual objects as examples
8/11/16

Portrait of Marcus Aurelius
Online Coins of the Roman Empire (OCRE) - Ontology based, knowledge base
http://numismatics.org/ocre/results
• Modeling in an ontology (formed in classes,
properties, relationships)
• Following Linked Data principles
• Using RDF triples for entities
• Querying in SPARQL language
8/11/16
39

40
Online Coins of the Roman Empire (OCRE)
http://numismatics.org/ocre/
• Using sparql queries to find
• Output as CSV files
• Auto-Visualizing using FusionTable
• Just needs a few seconds
8/11/16

8/11/16
41
Online Coins of the Roman Empire (OCRE)
http://numismatics.org/ocre/

42http://www.synaptica.com/oasis/
Deep Image Annotation
8/11/16

43Clarke, David. 2015. Deep image annotation and Knowledge Organization. ISKO-UK 2015.
/content/deep-image-annotation-and-knowledge-organization
8/11/16

44Clarke, David. 2015. Deep image annotation and Knowledge Organization. ISKO-UK 2015.
/content/deep-image-annotation-and-knowledge-organization
8/11/16

IIIF Image API
45See API specifications at: http://iiif.io/technical-details.html
International Image Interoperability Framework
Sanderson, Rob. 2014. Open Repositories 2014: Crowdsourced Transcription via IIIF, slide 9.
API= application programming interface, a set of routines, protocols, and
tools for building software applications.
8/11/16

III. The Importance of
Knowledge Organization Systems (KOS)
for Effective Subject Access
Various Types of KOS
1. Eliminating ambiguity
2. Controlling synonyms or
equivalents
3. Making explicit semantic
relationships
between/among concepts
Hierarchical relationships
hierarchical + other
associate
relationships
4. Presenting relationships
between/among concepts
as well as properties of
concepts
Fundamental
KOS Approaches
See full picture at http://nkos.slis.kent.edu/KOS_taxonomy.htm8/11/16
46

Figure. FRBR-LRM Overview of Relationships (draft)
Source: http://www.ifla.org/files/assets/cataloguing/frbr-lrm/frbr-lrm_20160225.pdf + revision draft
Dealing with The Problem of Semantic Conflicts
(inconsistencies in terminology and meanings)
8/11/16
47

48
Dealing with The Problem of Information Overload
Traditional Filters -- “Filter-out”
• Site (physical or digital) organization and navigation support
• Advanced search functions
• “Umbrella” structures of classification and taxonomy from which to
extend content
• Browsing support—hierarchical structures
Beyond traditional filters -- “Filter-forward”
• Browsing and Filtering to the Front -- Using Faceted Structure
• Connecting Things via Semantic Relations
• Enabling Rediscovery
– Data mining, semantic analysis, machine-learning through expert
feedback, machine reasoning
• LOD KOS Datasets become Knowledge Bases
– obtaining special graphs or datasets for very complicated questions, and
– revealing unknown relationships (e.g., http://vocab.getty.edu/queries#Top-
level_Subjects
• From Machine-readable to Machine-understandable/processable
8/11/16

In the BARTOC registry
KOS registered: 1836
in the Datahub
LOD KOS registered ：1251
(about a half are ontologies)
49
http://bartoc.org/
https://datahub.io/
(2016.05.27 data)
(2016.03.15 data)
Fact: The Increasing Need for KOS
8/11/16

Initiatives in digital humanities have demonstrated a paradigm shift in
how cultural heritage materials can be
- searched, mined, displayed, taught, and analyzed
utilizing digital technologies.
Data provided by LAMs and cultural heritage institutions are treasures for all
humanities researchers.
When subject access, smart data, and digital humanities interact, the
opportunity of effective and innovative services and contributions can be
endless.
Let’s embrace the new and changing concepts and make these happen.
Conclusion
Thank you!8/11/16
50

References
• Gardner, D. 2012. An ocean of data [Introduction]. In: Smolan, R., Erwitt, J. (eds.) The human face of big
data, pp. 14-17. Sausalito, CA: Against All Odds Productions.
• Joshi, Kunal. 2013. Big data, data science & fast data. http://www.slideshare.net/kunaljoshi111/big-data-
data-science-fast-data
• Kobielus, James. 2016. The Evolution of Big Data to Smart Data. Keynote at Smart Data Online 2016.
• Rose, Gillan. 2013. Visual Methodologies, 3rd. Edition. SAGE Publications Ltd.
• Sanderson, Rob. 2014. Open Repositories 2014: Crowdsourced Transcription via IIIF, slide 9.
http://www.slideshare.net/azaroth42/open-repositories-2014-crowdsourced-transcription-via-iiif
• SAS Institute Inc. [2014]. Big Data: What it is and why it matters. http://www.sas.com/big-data/
• Schöch, Christof. 2013. Big? Smart? Clean? Messy? Data in the humanities. Journal for Digital Humanities.
2(3): 2-13. http://journalofdigitalhumanities.org/2-3/big-smart-clean-messy-data-in-the-humanities/
• Sheth, Amit. 2014. Transforming Big Data into Smart Data: Deriving Value via harnessing Volume, Variety
and Velocity using semantics and Semantic Web. Keynote at 30th IEEE International Conference on Data
Engineering (ICDE) 2014.
• Svensson, Patrik. 2010. The landscape of digital humanities. Digital Humanities Quarterly. 4(1)
http://digitalhumanities.org/dhq/vol/4/1/000080/000080.html
• Svensson, Patrik. 2009. Humanities computing as digital humanities. Digital Humanities Quarterly. 3(3)
http://digitalhumanities.org/dhq/vol/3/3/000065/000065.html
• TiECON East. 2014. Data is new oil. http://www.tieconeast.org/2014/big-data-analytics
518/11/16

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