Discovering expansion entities for keyword-based entity search in linked data

There is an inherent rift between the characteristics of Web of documents and the Web of data - the latter is enriched with semantic properties that are not present in the former. This creates a formidable challenge for entity search in the era of Linked Data, requiring a new method that leverages on such features. Query expansion, used in keyword-based search, improves search quality by enhancing a query with terms. Existing query-expansion methods, statistical- and lexical-based approaches, are inadequate for linked data in two ways: (a) term-to-term co-occurrence, used in the statistical-based approach, cannot find satisfactory expansions in internal corpus (SPO triples) or external corpus (Web of documents); and (b) lexical incomparability between ontologies (or thesauri) as reference knowledge and linked data renders tenuous the possibility of creating lexically sound expanded queries. The study introduces a framework to expand keyword queries with expansion entities for keyword-based entity search in linked data. The framework offers two structures, star-shaped and multi-shaped RDF graphs (documents), to model the entities in linked data for indexing and search, and an algorithm called PFC for expansion entities by which to expand a given query. PFC obtains expansion entities by measuring a global importance (PageRank and entity-document Frequency) and a local importance (Centrality) of the candidates extracted from the returned RDF documents of the entity search. Our experiments illustrate that PFC improves search results by approximately 7%. This study also includes suggestions on how to glean important link types for extracting candidate expansion entities, as well as identifying properties of these entities by which to expand the query.