Entity Linking and Knowledge Graph Construction

A named entity recognizer might label Jordan as a person or a location. The tag alone does not identify the referent. To join the mention to a database record, you still need to decide whether it refers to Michael Jordan, the country, or another entity. Choosing the wrong record can attach otherwise correctly extracted facts to the wrong subject.

Recognition gives you a text span; linking assigns a knowledge-base identity. Strings can already be searched and joined, but equality of strings is an unreliable substitute for equality of referents. With correct links and distinct catalog IDs, different surface forms can contribute to the same entity record while identically spelled names for different entities remain separate.

Entity linking (EL) assigns text mentions to entries in a chosen knowledge base (KB). A linker can target Wikipedia articles, Wikidata items, biomedical concepts, or records in a private catalog. In this chapter, each catalog entry has an identifier and a description; aliases and graph relations provide additional evidence when available. These fields are useful design choices, rather than requirements that every knowledge base satisfies.

Knowledge graph construction (KGC) combines identity decisions with relation extraction and data management. Linking identifies the arguments. Relation extraction also needs a predicate, such as orbits in (mercury_planet, orbits, sun). A model can predict the arguments and predicate jointly or in separate stages. A construction workflow can validate candidates and resolve duplicate records under application rules while retaining supporting evidence. Provenance makes an assertion traceable; it does not establish that the source or the extractor was correct.

We study linking and construction together because their errors can propagate. A wrong linked argument can produce a wrong graph edge. In the other direction, graph compatibility can help disambiguate a mention, and an application constraint can flag an incompatible proposed edge. Such checks depend on the quality of the graph and the chosen validation rules.

Named entity recognition and BIO tagging from Named Entity Recognition and BIO Tagging provide a starting point for finding mention spans. Relation and event extraction from the preceding chapter provides candidate predicates and arguments. We reuse TF-IDF to explain context matching, then add entity identity and graph compatibility. The worked example compares a popularity baseline, local scoring, and joint scoring before building an inspectable assertion catalog.