A model for ranking entity attributes using DBpedia. Issue 5 (19th August 2014)

• Record Type: Journal Article
• Authors: Alahmari, Fahad; Thom, James A.; Magee, Liam
• Editors: Lewandowski, Dirk; Alexander, Fran
• Is Part Of: Aslib journal of information management. Volume 66:Issue 5(2014:Oct.)
• Publication Date: 2014-08-19
• Subjects: Information science -- Periodicals; Library science -- Periodicals; 020.5
• DOI: 10.1108/AJIM-12-2013-0148
• Languages: English
• ISSNs: 2050-3806

Abstract

Abstract : Purpose: Previous work highlights two key challenges in searching for information about individual entities (such as persons, places and organisations) over semantic data: query ambiguity and redundant attributes. This paper considers these challenges and proposes the Attribute Importance Model for clustering and ranking aggregated entity search to improve the overall users' experience of finding and navigating entities over the Web of Data. Design/methodology/approach: The proposed model describes three distinct techniques for augmenting semantic search: (1) presenting entity type-based query suggestions; (2) clustering aggregated attributes; and (3) ranking attributes based on their importance to a given query. To evaluate the model, 36 subjects were recruited to experience entity search with and without Attribute Importance Model. Findings: Our experimental results show that our model achieves significant improvements over the default method of semantic aggregated search provided by Sig.ma, a leading entity search and navigation tool. Originality/value: This proposal develops more informative views for aggregated entity search and exploration to enhance users' understanding of semantic data. The user study is the first to evaluate user interaction with Sig.ma's search capabilities in a systematic way.