DataTerminology.comNEW
Also known as: Knowledge Graph Construction, Knowledge Graph Modeling
The practice of modeling, integrating, and populating knowledge graphs from heterogeneous data sources.
DESCRIPTION
Knowledge graphing encompasses ontology design, schema mapping, entity reconciliation, and ingestion pipelines that create graph-structured knowledge bases. It uses standards like RDF, OWL, and SPARQL or property graph models in graph databases. The approach enables semantic queries, reasoning, and context-aware AI applications. Common steps include source profiling, schema alignment, reference data mapping, and link discovery. It supports enterprise data integration and AI features such as recommendations and question answering.
ALTERNATIVE DEFINITIONS
W3C
Knowledge graphs represent entities and their relationships, enabling integration and reasoning over heterogeneous data.
Stresses integration and reasoning with heterogeneous data.
Hogan et al., “Knowledge Graphs”
A knowledge graph is a graph of data intended to accumulate and convey knowledge about the world, with nodes representing entities and edges representing relationships.
Provides a precise, survey-based definition of knowledge graphs.
Neo4j
A knowledge graph organizes information into entities and relationships, making data easier to query, connect, and analyze.
Highlights practical benefits for querying and analysis in property graphs.
RELATED TERMS
Ontology Engineering RDF OWL Property Graph Entity Resolution SPARQL Graph Database Extract-Transform-Load Semantic Mapping
COMPETING TERMS
Relational Modeling Data Warehousing
KNOWLEDGE AREAS
Semantic & Knowledge Modeling AI Data Engineering Data Integration & Interoperability
TARGET AUDIENCE
AI Engineer Data Scientist Data Engineer Data Modeler Data Architect
EXTERNAL SOURCES
- Wikipedia - Knowledge graph
- W3C - RDF primer for graph-based data
- W3C - OWL 2 overview for ontologies
- Hogan et al. - Comprehensive survey on knowledge graphs
TOP QUESTIONS
What is the core workflow in Knowledge Graphing?
Profile sources, design ontology, align schemas, reconcile entities, generate triples or relationships, and validate before deployment with SPARQL or graph queries.
Category: Overview
When should you use RDF vs. property graphs in Knowledge Graphing?
Use RDF/OWL for semantic interoperability and reasoning; property graphs excel in flexible, developer-centric graph analytics. Some architectures combine both.
Category: Opinion
What risks exist in Knowledge Graphing projects?
Poor ontology design, inadequate entity resolution, and missing provenance can lead to brittle graphs and mistrust. Governance and quality checks are essential.
Category: Risks
How does Knowledge Graphing boost AI applications?
It adds context, constraints, and explainability to models, powering features like recommendations, semantic search, and question answering.
Category: Relevancy
What are common mistakes in Knowledge Graphing?
Over-modeling without use cases, ignoring identifiers, and skipping schema mapping validation often cause complexity and errors.
Category: Mistakes
What standards support Knowledge Graphing?
RDF, OWL, RDFS, SKOS, and SPARQL, along with SHACL for validation and JSON-LD for linked data serialization.
Category: Requirements
How to measure success of a Knowledge Graph?
Track query performance, coverage of key entities, linkage quality, user adoption, and impact on downstream AI tasks.
Category: Opinion
Where to start learning Knowledge Graphing?
Read W3C RDF/OWL primers, study Hogan et al.’s survey, and build a small graph with public datasets to practice modeling and linking.
Category: Requirements
What is the biggest misunderstanding about Knowledge Graphing?
It’s not just ingesting data into a graph; it requires careful semantic modeling, identifiers, and governance for durability and reuse.
Category: Misunderstandings