Knowledge Graphs Applied - MEAP Version 2

Chapters 1-3,5Knowledge Graphs Applied is a practical guide to putting knowledge graphs into action. It’s full of techniques and code samples for building and analyzing knowledge graphs, all demonstrated with serious full-sized datasets. Knowledge Graphs Applied MEAP V02 Copyright Welcome Brief contents Chapter 1: What is a knowledge graph? 1.1 The knowledge graph paradigm shift 1.1.1 The four pillars of knowledge graphs 1.2 Building data-driven applications using KGs 1.2.1 360-based view for precision medicine 1.2.2 Drug discovery and development 1.2.3 Healthcare compliance management 1.2.4 Conversational AI and recommendation systems 1.2.5 What should I ask myself? 1.3 How do we teach knowledge graphs? 1.4 Knowledge graph technologies 1.5 Making graphs smarter using semantics 1.5.1 Graph vs. knowledge graph 1.5.2 Taxonomies and ontologies 1.6 Summary 1.7 References Chapter 2: Intelligent systems 2.1 Designing a first intelligent system 2.1.1 What is an intelligent system? 2.1.2 Categories of intelligent systems 2.1.3 Characteristics of an intelligent system 2.2 Knowledge acquisition 2.3 Knowledge representation and reasoning 2.4 Reasoning engines 2.5 The role of knowledge graphs 2.6 Summary 2.7 References Chapter 3: Create your first knowledge graph from ontologies 3.1 Knowledge graph building: Warm-up 3.1.1 Business and domain understanding 3.1.2 Data understanding 3.2 Understanding knowledge graph technologies 3.2.1 RDF or LPG? A goal-driven discussion 3.2.2 Representing edge properties with RDF and LPG 3.3 Knowledge graph building 3.3.1 Ontology ingestion and processing with neosemantics 3.3.2 Dataset ingestion and processing 3.4 Querying the data 3.5 Reasoning over the knowledge graph 3.6 Summary 3.7 References Chapter 5: Knowledge graphs (KGs) and natural language processing (NLP) 5.1 What is natural language processing (NLP)? 5.1.1 Basics of natural language processing 5.1.2 Named Entity Recognition (NER) 5.1.3 Use NLP for building a first KG 5.2 Knowledge enrichment 5.3 NLP-based machine learning 5.3.1 Keyword extraction 5.3.2 Graph-based topic modeling 5.4 Summary 5.5 References Appendix A: Introduction to graphs A.1 What is a graph? A.2 Graphs as models of networks A.3 Representing graphs A.4 References Appendix B: Neo4j B.1 Neo4j Introduction B.2 Neo4j Installation B.2.1 Neo4j Server installation B.2.2 Neo4j Desktop installation B.3 Cypher B.4 Plugins installation B.4.1 APOC installation B.4.2 GDS installation B.5 Cleaning B.6 References