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A Librarian's Guide to Graphs, Data and the Semantic Web

Book Description

Graphs are about connections, and are an important part of our connected and data-driven world. A Librarian's Guide to Graphs, Data and the Semantic Web is geared toward library and information science professionals, including librarians, software developers and information systems architects who want to understand the fundamentals of graph theory, how it is used to represent and explore data, and how it relates to the semantic web. This title provides a firm grounding in the field at a level suitable for a broad audience, with an emphasis on open source solutions and what problems these tools solve at a conceptual level, with minimal emphasis on algorithms or mathematics. The text will also be of special interest to data science librarians and data professionals, since it introduces many graph theory concepts by exploring data-driven networks from various scientific disciplines. The first two chapters consider graphs in theory and the science of networks, before the following chapters cover networks in various disciplines. Remaining chapters move on to library networks, graph tools, graph analysis libraries, information problems and network solutions, and semantic graphs and the semantic web.

  • Provides an accessible introduction to network science that is suitable for a broad audience
  • Devotes several chapters to a survey of how graph theory has been used in a number of scientific data-driven disciplines
  • Explores how graph theory could aid library and information scientists

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. About the authors
  7. Preface and Acknowledgments
  8. Introduction
    1. Glossary
  9. 1. Graphs in theory
    1. Bridging the history
    2. Topology
    3. Degrees of separation
    4. Four color problem
  10. 2. Graphs and how to make them
    1. Space junk and graph theory
    2. Graph theory and graph modeling
    3. Analyzing graphs
  11. 3. Graphs and the Semantic Web
    1. “…memory is transitory”
    2. The RDF model
    3. Modeling triples
    4. RDF and deduction
  12. 4. RDF and its serializations
    1. Abstract notions lead to shared concepts
    2. RDF graph
    3. RDF serializations
  13. 5. Ontologies
    1. Ontological autometamorphosis
    2. Introduction to ontologies
    3. Building blocks of ontologies
    4. Ontology building tutorial
    5. Ontologies and logic
  14. 6. SPARQL
    1. Triple patterns for search
    2. SPARQL
    3. SPARQL query endpoint
    4. SPARQL 1.1
  15. 7. Inferencing, reasoning, and rules
    1. Mechanical thought
    2. Intelligent computers
    3. Language to logic
    4. Inferencing
    5. Logic notation
    6. Challenges and pitfalls of rules
    7. Reasoners and rules
    8. SWRL
    9. N3 rules
    10. Final considerations
  16. 8. Understanding Linked Data
    1. Demons and genies
    2. Characteristics of Linked Data
    3. Discovering Linked Open Data
    4. Linked Open Vocabularies
    5. Linked Data platform
  17. 9. Library networks—coauthorship, citation, and usage graphs
    1. “Uncritical citation…is a serious matter”
    2. History and evolution of science
    3. Librarians as network navigators
    4. Author metrics and networks
    5. Analyzing coauthorship networks
  18. 10. Networks in life sciences
    1. The path of an infection
    2. Food webs and motifs
  19. 11. Biological networks
    1. DNA is software
    2. Comparing networks
    3. A fresh perspective
  20. 12. Networks in economics and business
    1. Look at the systems, not the individuals
    2. Information flow
    3. Is it contagious?
    4. The city effect
  21. 13. Networks in chemistry and physics
    1. The best T-shirts graph theory has to offer
    2. Percolation
    3. Phase transitions
    4. Synchronization
    5. Quantum interactions and crystals
  22. 14. Social networks
    1. Six degrees of separation
    2. It’s a small world
    3. Social network analysis
  23. 15. Upper ontologies
    1. A unifying framework for knowledge
    2. Friend of a Friend
    3. Organization
    4. Event
    5. Provenance
    6. Aggregations
    7. Data Sets
    8. Thesaurus
    9. Measurements
    10. Geospatial
    11. Geonames
    12. WGS84
    13. Spatial
  24. 16. Library metadata ontologies
    1. Where are the books?
    2. Migrating descriptions of library resources to RDF
    3. Pioneering Semantic Web projects in libraries
    4. The British Library
    5. UCSD Library Digital Asset Management System
    6. Linked data services
    7. Where to go from here?
  25. 17. Time
    1. Time flies
    2. Standard time
    3. Allen’s Temporal Intervals
    4. Semantic time
    5. Graph time
  26. 18. Drawing and serializing graphs
    1. The inscrutable hairball
    2. Graph Data Formats
    3. GDF
    4. XML and graphs
    5. XGMML
    6. GraphML
    7. GEXF
    8. JSON for D3
    9. GraphSON
    10. Graph visualization
    11. Graph layouts
    12. Force-directed layout
    13. Topological layouts
    14. Cytoscape
    15. Gephi
    16. GUESS
    17. Javascript libraries for graphs on the web
  27. 19. Graph analytics techniques
    1. Linux and food poisoning
    2. Why analyze entire graphs?
    3. Node degree measures
    4. Path analysis
    5. Clusters, partitions, cliques, motifs
    6. Graph structure and metrics
  28. 20. Graph analytics software libraries
    1. A note about RDF and graph analytics
    2. Jung
    3. JGraphT
    4. NetworkX
    5. Graph Edit Distance
  29. 21. Semantic repositories and how to use them
    1. VIVO
    2. Triplestores
    3. Inferencing and reasoning
    4. SPARQL 1.1 HTTP and Update
    5. Jena
    6. OpenRDF Sesame API
  30. 22. Graph databases and how to use them
    1. Thinking graphs
    2. Graph databases
    3. HeliosJS
    4. Titan
    5. Neo4J
    6. TinkerPop3
  31. 23. Case studies
    1. Case study 1 InfoSynth: a semantic web application for exploring integrated metadata collections
    2. Example use cases
    3. Technology
    4. Design and modeling
    5. Implementation
    6. Case Study 2 EgoSystem: a social network aggregation tool
    7. Example use cases
    8. Technology
    9. Design and modeling
    10. Implementation
  32. Index