A Librarian s Guide to Graphs Data and the Semantic Web 1st Edition by James Powell, Matthew Hopkins – Ebook PDF Instant Download/Delivery: 1843347539, 9781843347538
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Product details:
ISBN 10: 1843347539
ISBN 13: 9781843347538
Author: James Powell, Matthew Hopkins
- 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
A Librarian s Guide to Graphs Data and the Semantic Web 1st Table of contents:
1. Graphs in theory
Bridging the history
Topology
Degrees of separation
Four color problem
2. Graphs and how to make them
Space junk and graph theory
Graph theory and graph modeling
Analyzing graphs
3. Graphs and the Semantic Web
“…memory is transitory”
The RDF model
Modeling triples
RDF and deduction
4. RDF and its serializations
Abstract notions lead to shared concepts
RDF graph
RDF serializations
5. Ontologies
Ontological autometamorphosis
Introduction to ontologies
Building blocks of ontologies
Ontology building tutorial
Ontologies and logic
6. SPARQL
Triple patterns for search
SPARQL
SPARQL query endpoint
SPARQL 1.1
7. Inferencing, reasoning, and rules
Mechanical thought
Intelligent computers
Language to logic
Inferencing
Logic notation
Challenges and pitfalls of rules
Reasoners and rules
SWRL
N3 rules
Final considerations
8. Understanding Linked Data
Demons and genies
Characteristics of Linked Data
Discovering Linked Open Data
Linked Open Vocabularies
Linked Data platform
9. Library networks—coauthorship, citation, and usage graphs
“Uncritical citation…is a serious matter”
History and evolution of science
Librarians as network navigators
Author metrics and networks
Analyzing coauthorship networks
10. Networks in life sciences
The path of an infection
Food webs and motifs
11. Biological networks
DNA is software
Comparing networks
A fresh perspective
12. Networks in economics and business
Look at the systems, not the individuals
Information flow
Is it contagious?
The city effect
13. Networks in chemistry and physics
The best T-shirts graph theory has to offer
Percolation
Phase transitions
Synchronization
Quantum interactions and crystals
14. Social networks
Six degrees of separation
It’s a small world
Social network analysis
15. Upper ontologies
A unifying framework for knowledge
Friend of a Friend
Organization
Event
Provenance
Aggregations
Data Sets
Thesaurus
Measurements
Geospatial
Geonames
WGS84
Spatial
16. Library metadata ontologies
Where are the books?
Migrating descriptions of library resources to RDF
Pioneering Semantic Web projects in libraries
The British Library
UCSD Library Digital Asset Management System
Linked data services
Where to go from here?
17. Time
Time flies
Standard time
Allen’s Temporal Intervals
Semantic time
Graph time
18. Drawing and serializing graphs
The inscrutable hairball
Graph Data Formats
GDF
XML and graphs
XGMML
GraphML
GEXF
JSON for D3
GraphSON
Graph visualization
Graph layouts
Force-directed layout
Topological layouts
Cytoscape
Gephi
GUESS
Javascript libraries for graphs on the web
19. Graph analytics techniques
Linux and food poisoning
Why analyze entire graphs?
Node degree measures
Path analysis
Clusters, partitions, cliques, motifs
Graph structure and metrics
20. Graph analytics software libraries
A note about RDF and graph analytics
Jung
JGraphT
NetworkX
Graph Edit Distance
21. Semantic repositories and how to use them
VIVO
Triplestores
Inferencing and reasoning
SPARQL 1.1 HTTP and Update
Jena
OpenRDF Sesame API
22. Graph databases and how to use them
Thinking graphs
Graph databases
HeliosJS
Titan
Neo4J
TinkerPop3
23. Case studies
Case study 1 InfoSynth: a semantic web application for exploring integrated metadata collections
Example use cases
Technology
Design and modeling
Implementation
Case Study 2 EgoSystem: a social network aggregation tool
Example use cases
Technology
Design and modeling
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Tags: James Powell, Matthew Hopkins, Librarian s Guide, Graphs Data, Semantic Web