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Procedure Model for Building Knowledge Graphs for Industry Applications

Sascha Meckler

TL;DR

This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case.

Abstract

Enterprise knowledge graphs combine business data and organizational knowledge by means of a semantic network of concepts, properties, individuals and relationships. The graph-based integration of previously unconnected information with domain knowledge provides new insights and enables intelligent business applications. However, knowledge graph construction is a large investment which requires a joint effort of domain and technical experts. This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case. The self-contained process adapts the "Cross Industry Standard Process for Data Mining" and uses competency questions throughout the entire development cycle. The procedure model starts with business and data understanding, describes tasks for ontology modeling and the graph setup, and ends with process steps for evaluation and deployment.

Procedure Model for Building Knowledge Graphs for Industry Applications

TL;DR

This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case.

Abstract

Enterprise knowledge graphs combine business data and organizational knowledge by means of a semantic network of concepts, properties, individuals and relationships. The graph-based integration of previously unconnected information with domain knowledge provides new insights and enables intelligent business applications. However, knowledge graph construction is a large investment which requires a joint effort of domain and technical experts. This paper presents a practical step-by-step procedure model for building an RDF knowledge graph that interconnects heterogeneous data and expert knowledge for an industry use case. The self-contained process adapts the "Cross Industry Standard Process for Data Mining" and uses competency questions throughout the entire development cycle. The procedure model starts with business and data understanding, describes tasks for ontology modeling and the graph setup, and ends with process steps for evaluation and deployment.
Paper Structure (30 sections, 4 figures, 7 tables)

This paper contains 30 sections, 4 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Procedure Model
  3. Business Understanding
  4. Process Discovery
  5. Competency Questions
  6. Data Understanding
  7. Data Quality Evaluation
  8. Data Preparation
  9. Denormalization
  10. Modeling
  11. Ontology Development Methodology
  12. Ontology Design Patterns
  13. Practical Ontology Modeling
  14. Ontology Documentation
  15. Graph Setup
  16. ...and 15 more sections

Figures (4)

  • Figure 1: Procedure model for building an RDF KG for industry applications
  • Figure 2: Steps for ontology development derived from ontology101
  • Figure 3: Control loop that interconnects user requirements, competency questions, queries and the evaluation process.
  • Figure 4: Consolidated KG development process by KG-devprocess-survey; replica figure with sub-steps added to step "identify data"