This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.
The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques--based on statistics, graph analytics, machine learning, etc.--can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve.
This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
Author(s): Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
Series: Synthesis Lectures on Data, Semantics, and Knowledge
Edition: 1
Publisher: Morgan & Claypool
Year: 2021
Language: English
Commentary: Vector PDF
Pages: 257
City: San Rafael, CA
Tags: Reasoning; Ontologies; Social Networks; Graph Theory; Graph Algorithms; Graph Neural Networks; Data Graphs; Knowledge Graphs; Graph Analytics; Symbolic Learning; Quality Assessment
Preface
Acknowledgments
Introduction
Data Graphs
Models
Directed Edge-Labeled Graphs
Heterogeneous Graphs
Property Graphs
Graph Dataset
Other Graph Data Models
Graph Stores
Querying
Basic Graph Patterns
Complex Graph Patterns
Navigational Graph Patterns
Other Features
Query Interfaces
Schema, Identity, and Context
Schema
Semantic Schema
Validating Schema
Emergent Schema
Identity
Persistent Identifiers
External Identity Links
Datatypes
Lexicalization
Existential Nodes
Context
Direct Representation
Reification
Higher-Arity Representation
Annotations
Other Contextual Frameworks
Deductive Knowledge
Ontologies
Interpretations and Models
Ontology Features
Entailment
If-Then vs. If-and-Only-If Semantics
Reasoning
Rules
Description Logics
Inductive Knowledge
Graph Analytics
Techniques
Frameworks
Analytics on Data Graphs
Analytics with Queries
Analytics with Entailment
Knowledge Graph Embeddings
Tensor-Based Models
Language Models
Entailment-Aware Models
Graph Neural Networks
Recursive Graph Neural Networks
Non-Recursive Graph Neural Networks
Symbolic Learning
Rule Mining
Axiom Mining
Hypothesis Mining
Creation and Enrichment
Human Collaboration
Text Sources
Pre-Processing
Named Entity Recognition (NER)
Entity Linking (EL)
Relation Extraction (RE)
Joint Tasks
Markup Sources
Wrapper-Based Extraction
Web Table Extraction
Deep Web Crawling
Structured Sources
Mapping from Tables
Mapping from Trees
Mapping from Other Knowledge Graphs
Schema/Ontology Creation
Ontology Engineering
Ontology Learning
Quality Assessment
Accuracy
Syntactic Accuracy
Semantic Accuracy
Timeliness
Coverage
Completeness
Representativeness
Coherency
Consistency
Validity
Succinctness
Conciseness
Representational Conciseness
Understandability
Other Quality Dimensions
Refinement
Completion
General Link Prediction
Type-Link Prediction
Identity-Link Prediction
Correction
Fact Validation
Inconsistency Repairs
Other Refinement Tasks
Publication
Best Practices
FAIR Principles
Linked Data Principles
Access Protocols
Dumps
Node Lookups
Edge Patterns
(Complex) Graph Patterns
Other Protocols
Usage Control
Licensing
Usage Policies
Encryption
Anonymization
Knowledge Graphs in Practice
Open Knowledge Graphs
DBpedia
Yet Another Great Ontology
Freebase
Wikidata
Other Open Cross-Domain Knowledge Graphs
Domain-Specific Open Knowledge Graphs
Enterprise Knowledge Graphs
Web Search
Commerce
Social Networks
Finance
Other Industries
Conclusions
Background
Historical Perspective
``Knowledge Graphs:'' Pre-2012
``Knowledge Graphs:'' 2012 Onward
Bibliography
Authors' Biographies
