[[edit](/w/index.php?title=Knowledge_graph&action=edit&section=5 "Edit section: Entity alignment")] [](https://en.wikipedia.org/wiki/File:Knowledge_graph_entity_alignment.png) Two hypothetical knowledge graphs representing disparate topics contain a node that corresponds to the same entity in the real world. Entity alignment is the process of identifying such nodes across multiple graphs. As new knowledge graphs are produced across a variety of fields and contexts, the same entity will inevitably be represented in multiple graphs. However, because no single standard for the construction or representation of knowledge graph exists, resolving which entities from disparate graphs correspond to the same real world subject is a non-trivial task. This task is known as *knowledge graph entity alignment*, and is an active area of research.[[38]](#cite_note-38) Strategies for entity alignment generally seek to identify similar substructures, semantic relationships, shared attributes, or combinations of all three between two distinct knowledge graphs.[[39]](#cite_note-39) Entity alignment methods use these structural similarities between generally non-isomorphic graphs to predict which nodes correspond to the same entity.[[40]](#cite_note-40) In 2023, researchers found success in using large language models (LLMs) in the task of entity alignment. [[41]](#cite_note-41) This was in particular thanks to their effectiveness at producing syntactically meaningful embeddings.[[42]](#cite_note-42) As the amount of data stored in knowledge graphs grows, developing dependable methods for knowledge graph entity alignment becomes an increasingly crucial step in the integration and cohesion of knowledge graph data. See also -------- [[edit](/w/index.php?title=Knowledge_graph&action=edit&section=6 "Edit section: See also")] * [Concept map](https://en.wikipedia.org/wiki/Concept_map "Concept map") – Diagram showing relationships among concepts * [Formal semantics (natural language)](https://en.wikipedia.org/wiki/Formal_semantics_(natural_language) "Formal semantics (natural language)") – Formal study of linguistic meaning * [Graph database](https://en.wikipedia.org/wiki/Graph_database "Graph database") – Database using graph structures for queries * [Knowledge base](https://en.wikipedia.org/wiki/Knowledge_base "Knowledge base") – Information repository with multiple applications * [Knowledge graph embedding](https://en.wikipedia.org/wiki/Knowledge_graph_embedding "Knowledge graph embedding") – Dimensionality reduction of graph-based semantic data objects [machine learning task] * [Logical graph](https://en.wikipedia.org/wiki/Logical_graph "Logical graph") – Type of diagrammatic notation for propositional logic * [Semantic integration](https://en.wikipedia.org/wiki/Semantic_integration "Semantic integration") – Interrelating info from diverse sources * [Semantic technology](https://en.wikipedia.org/wiki/Semantic_technology "Semantic technology") – Technology to help machines understand data * [Topic map](https://en.wikipedia.org/wiki/Topic_map "Topic map") – Knowledge organization system * [Vadalog](https://en.wikipedia.org/wiki/Vadalog "Vadalog") – Type of Knowledge Graph Management System * [Wikibase](https://en.wikipedia.org/wiki/Wikibase "Wikibase")- Mediawiki Software extensions for creating knowledge bases * [Wikidata](https://en.wikipedia.org/wiki/Wikidata "Wikidata") - Free Knowledge Database Project * [YAGO (database)](https://en.wikipedia.org/wiki/YAGO_(database) "YAGO (database)") – Open-source information repository References ---------- [[edit](/w/index.php?title=Knowledge_graph&action=edit&section=7 "Edit section: References")]

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  "text": "[[edit](/w/index.php?title=Knowledge_graph&action=edit&section=5 \"Edit section: Entity alignment\")]\n\n[![](//upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Knowledge_graph_entity_alignment.png/500px-Knowledge_graph_entity_alignment.png)](https://en.wikipedia.org/wiki/File:Knowledge_graph_entity_alignment.png)\n\nTwo hypothetical knowledge graphs representing disparate topics contain a node that corresponds to the same entity in the real world. Entity alignment is the process of identifying such nodes across multiple graphs.\n\nAs new knowledge graphs are produced across a variety of fields and contexts, the same entity will inevitably be represented in multiple graphs. However, because no single standard for the construction or representation of knowledge graph exists, resolving which entities from disparate graphs correspond to the same real world subject is a non-trivial task. This task is known as *knowledge graph entity alignment*, and is an active area of research.[[38]](#cite_note-38)\n\nStrategies for entity alignment generally seek to identify similar substructures, semantic relationships, shared attributes, or combinations of all three between two distinct knowledge graphs.[[39]](#cite_note-39) Entity alignment methods use these structural similarities between generally non-isomorphic graphs to predict which nodes correspond to the same entity.[[40]](#cite_note-40)\n\nIn 2023, researchers found success in using large language models (LLMs) in the task of entity alignment. [[41]](#cite_note-41) This was in particular thanks to their effectiveness at producing syntactically meaningful embeddings.[[42]](#cite_note-42)\n\nAs the amount of data stored in knowledge graphs grows, developing dependable methods for knowledge graph entity alignment becomes an increasingly crucial step in the integration and cohesion of knowledge graph data.\n\nSee also\n--------\n\n[[edit](/w/index.php?title=Knowledge_graph&action=edit&section=6 \"Edit section: See also\")]\n\n* [Concept map](https://en.wikipedia.org/wiki/Concept_map \"Concept map\") – Diagram showing relationships among concepts\n* [Formal semantics (natural language)](https://en.wikipedia.org/wiki/Formal_semantics_(natural_language) \"Formal semantics (natural language)\") – Formal study of linguistic meaning\n* [Graph database](https://en.wikipedia.org/wiki/Graph_database \"Graph database\") – Database using graph structures for queries\n* [Knowledge base](https://en.wikipedia.org/wiki/Knowledge_base \"Knowledge base\") – Information repository with multiple applications\n* [Knowledge graph embedding](https://en.wikipedia.org/wiki/Knowledge_graph_embedding \"Knowledge graph embedding\") – Dimensionality reduction of graph-based semantic data objects [machine learning task]\n* [Logical graph](https://en.wikipedia.org/wiki/Logical_graph \"Logical graph\") – Type of diagrammatic notation for propositional logic\n* [Semantic integration](https://en.wikipedia.org/wiki/Semantic_integration \"Semantic integration\") – Interrelating info from diverse sources\n* [Semantic technology](https://en.wikipedia.org/wiki/Semantic_technology \"Semantic technology\") – Technology to help machines understand data\n* [Topic map](https://en.wikipedia.org/wiki/Topic_map \"Topic map\") – Knowledge organization system\n* [Vadalog](https://en.wikipedia.org/wiki/Vadalog \"Vadalog\") – Type of Knowledge Graph Management System\n* [Wikibase](https://en.wikipedia.org/wiki/Wikibase \"Wikibase\")- Mediawiki Software extensions for creating knowledge bases\n* [Wikidata](https://en.wikipedia.org/wiki/Wikidata \"Wikidata\") - Free Knowledge Database Project\n* [YAGO (database)](https://en.wikipedia.org/wiki/YAGO_(database) \"YAGO (database)\") – Open-source information repository\n\nReferences\n----------\n\n[[edit](/w/index.php?title=Knowledge_graph&action=edit&section=7 \"Edit section: References\")]",
  "access_class": "public",
  "heading": "Entity alignment (part 1 of 4)",
  "observed_at": "2026-07-18T21:40:59.214Z",
  "state": "active"
}