Editing Knowledge Graphs (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert evaluation of knowledge graphs is multi-dimensional:

'''Factual accuracy''': Randomly sample triples and verify against authoritative sources. Precision is the primary metric for knowledge graphs serving downstream systems.

'''Coverage / recall''': For a specific domain, what fraction of known true facts are represented? Measured by comparing against a held-out set of verified triples.

'''Link prediction benchmarks''': FB15k-237 (Freebase subset) and WN18RR (WordNet subset) are standard benchmarks for evaluating knowledge graph embedding methods. Metrics: Mean Reciprocal Rank (MRR), Hits@10.

'''Query performance''': For production KGs, SPARQL query execution time at p95 for typical query patterns. Neo4j and other property graph DBs provide query profiling tools.

'''Downstream task impact''': Does using the KG improve performance on the target application (question answering, recommendation, entity disambiguation)? This is the ultimate measure of KG quality.

Expert practitioners also evaluate '''provenance and freshness''': For each triple, is its source known and trusted? How recently was it validated? Temporal knowledge graphs additionally track when facts were true, enabling time-sensitive queries.
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