3 Create your first Knowledge Graph from ontologies

 

This chapter covers

  • Building your first knowledge graph starting with business goals
  • Selecting the best technology for the knowledge graph representation based on use cases
  • Constructing a valuable knowledge graph to support clinicians' activity
  • Performing analysis and ontology-based reasoning on top of this knowledge graph

The KG construction is complex because it requires extracting and integrating information from various data sources. We can relate the differences among these sources to the data format (XML, CSV, or JSON), the storage technology (relational or document-oriented databases), and the information syntax (2022-08-09 or 9 August 2022). Another key difference involves the meaning of the data, which becomes critical for information exchange. In the case of healthcare, for instance, the adoption of diverse expressions to identify the same concept (type 2 diabetes or ketosis-resistant diabetes), the use of the same acronym to define distinct concepts (PE for physical examination or pulmonary embolism), and varying levels of information granularity (necrosis or lobular necrosis) present significant obstacles to data integration.

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 Annotations ingestion and processing

3.4 Querying the data

3.5 Reasoning over the knowledge graph

3.6 Summary

3.7 References