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Concept for semantic modeling of entities and relationships that links, contextualizes and makes data machine-readable.
Knowledge graphs are structured, semantic representations of entities and their relationships that connect and contextualize data from diverse sources. They enable querying, inference and integration of heterogeneous data for analytics, search and knowledge management. Typical applications include enterprise data integration, semantic search and recommendation systems.
Average response time of semantic queries.
Share of relevant entities represented in the graph.
Average number of relationships per entity.
Large-scale knowledge base used to improve search and entity resolution.
Open, collaborative knowledge base of linked entities with extensive ontology.
Extraction of structured information from Wikipedia for research and integration.
Define use cases and core entities
Select a graph backend and standards
Implement mappings, linking and APIs