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Chapter and Conference Paper
Navigating Ontology Development with Large Language Models
Ontology engineering is a complex and time-consuming task, even with the help of current modelling environments. Often the result is error-prone unless developed by experienced ontology engineers. However, wit...
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Chapter
Refinement
Beyond assessing the quality of a knowledge graph, there exist techniques to refine the knowledge graph, in particular to (semi-)automatically complete and correct the knowledge graph [Paul-heim, 2017], aka knowl...
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Chapter
Conclusions
We have provided a comprehensive introduction to knowledge graphs, which have been receiving more and more attention in recent years. Under the definition of a knowledge graph as a graph ofdata intended to accumu...
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Chapter
Introduction
Though the phrase “knowledge graph” has been used in the literature since at least 1972 [Schneider, 1973], the modern incarnation of the phrase stems from the 2012 announcement of the Google Knowledge Graph [S...
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Chapter
Quality Assessment
Independent of the (kinds of) source(s) from which a knowledge graph is created, the resulting initial knowledge graph will usually be incomplete, and will often contain duplicate, contradictory or even incorr...
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Chapter
Knowledge Graphs in Practice
In this chapter, we discuss some of the most prominent knowledge graphs that have emerged in the past years. We begin by discussing open knowledge graphs, most of which have been published on the Web per the g...
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Chapter
Creation and Enrichment
In this chapter, we discuss the principal techniques by which knowledge graphs can be created and subsequently enriched from diverse sources of legacy data that range from plain text to structured formats (and...
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Book
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Chapter
Schema, Identity, and Context
In this chapter we describe extensions of the data graph–relating to schema, identity, and context–that provide additional structures for accumulating knowledge. Henceforth, we refer to a data graph as a collecti...
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Chapter
Inductive Knowledge
While deductive knowledge is characterized by precise logical consequences, inductively acquiring knowledge involves generalizing patterns from a given set of input observations, which can then be used to gene...
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Chapter
Publication
While it may not always be desirable to publish knowledge graphs (for example, those that offer a competitive advantage to a company [Noy et al., 2019]), it maybe desirable or even required to publish other kn...
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Chapter
Data Graphs
At the foundation of any knowledge graph is the principle of first applying a graph abstraction to data, resulting in an initial data graph. We now discuss a selection of graph-structured data models that are ...
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Chapter
Deductive Knowledge
As humans, we can deduce more from the data graph of Figure 2.1 than what the edges explicitly indicate. We may deduce, for example, that the $${\rm{\...
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Chapter and Conference Paper
SPIRIT: Semantic and Systemic Interoperability for Identity Resolution in Intelligence Analysis
This paper introduces the SPIRIT H2020 Project. The SPIRIT identity resolution service has been designed to learn about identity patterns, to build up a social graph related to them, and thereby facilitate LEA...
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Book and Conference Proceedings
Semantic Systems. In the Era of Knowledge Graphs
16th International Conference on Semantic Systems, SEMANTiCS 2020, Amsterdam, The Netherlands, September 7–10, 2020, Proceedings
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Chapter and Conference Paper
Capturing and Querying Uncertainty in RDF Stream Processing
RDF Stream Processing (RSP) has been proposed as a candidate for bringing together the Complex Event Processing (CEP) paradigm and the Semantic Web standards. In this paper, we investigate the impact of explic...
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Reference Work Entry In depth
Ontologies for Big Data
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Chapter and Conference Paper
RSP-QL \(^{\star }\!\) : Enabling Statement-Level Annotations in RDF Streams
RSP-QL was developed by the W3C RDF Stream Processing (RSP) community group as a common way to express and query RDF streams. However, RSP-QL does not provide any way of annotating data on the statement level...
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Living Reference Work Entry In depth
Ontologies for Big Data
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Book and Conference Proceedings
Semantic Web Challenges
4th SemWebEval Challenge at ESWC 2017, Portoroz, Slovenia, May 28 - June 1, 2017, Revised Selected Papers