Editing Knowledge Graphs (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Knowledge Representation Comparison
! Approach !! Structure !! Reasoning !! Scalability !! Interpretability
|-
| Relational database || Tables, rows || SQL (closed world) || Very high || High
|-
| Knowledge graph (RDF) || Triples || SPARQL, inference rules || High || High
|-
| Property graph (Neo4j) || Nodes + edges with properties || Cypher, path queries || High || High
|-
| Vector embeddings alone || Implicit in weights || Neural similarity || Very high || Low
|-
| Knowledge graph + embeddings || Hybrid || Symbolic + neural || High || Medium
|}

'''Key challenges and failure modes:'''
* '''Incompleteness''' — Even the largest knowledge graphs (Wikidata: 100M+ triples) are dramatically incomplete. Most entity-relation-entity combinations that are true are not represented.
* '''Inconsistency''' — Different sources record conflicting information. Conflict resolution and provenance tracking are essential but difficult.
* '''Coverage-precision trade-off''' — Manual curation is precise but slow and incomplete; automated extraction has high recall but introduces errors.
* '''Schema evolution''' — As understanding of a domain evolves, the ontology needs updating, which can invalidate existing triples.
* '''Entity ambiguity''' — "Apple" could be the company, the fruit, or countless others. Entity linking (mapping text mentions to KG entities) is difficult and error-prone.
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