Semantic Integration in Text: From Ambiguous Names to Identifiable Entities

The topic has been widely studied in the context of structured data, with problems being considered including ontology and schema matching, matching relational tuples, and reconciling inconsistent data values. This article studies a key challenge in semantic integration over text: identifying whether different mentions of real-world entities, such as "JFK" and "John Kennedy," within and across natural language text documents, actually represent the same concept. In its most general form, our model assumes (1) a joint distribution over entities (for example, a document that mentions "President Kennedy" is more likely to mention "Oswald" or "White House" than "Roger Clemens"), and (2) an "author" model that assumes that at least one mention of an entity in a document is easily identifiable and then generates other mentions via (3) an "appearance" model that governs how mentions are transformed from the "representative" mention. Finally, we discuss how our solution for mention matching in text can be potentially applied to matching relational tuples, as well as to linking entities across databases and text.