Discovering and Ranking New Links for Linked Data Supplier

Zong, Nansu; Yang, Sung-Kwon; Namgoong, Hyun; Kim, Hong-Gee

doi:10.1007/978-3-642-29923-0_25

Cited by 1 publication

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“…Three tasks are involved in the study to achieve our goal: (a) how to find expansion entities by which to expand a query; (b) which links in RDF documents play an important role in extracting effective expansion entities; and (c) how to model expanded queries with expansion entities. To achieve the first task, we have extended the work in Zong et al [15], which explores related entities or links in order to help new linked data suppliers to find new alignments. We propose a framework to help existing keyword-based entity search engines, such as Sindice [16], to improve search by expanding queries with expansion entities (the expansion entities denote entities used to expand a query).…”

Section: Introductionmentioning

confidence: 99%

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

Zong

Lee

Kim

2015

Journal of Information Science

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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.

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Section: Introductionmentioning

confidence: 99%