Article
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A Purely Entity-Based Semantic Search Approach for Document Retrieval
Version 1
: Received: 15 August 2023 / Approved: 16 August 2023 / Online: 17 August 2023 (09:47:09 CEST)
A peer-reviewed article of this Preprint also exists.
Sidi, M.L.; Gunal, S. A Purely Entity-Based Semantic Search Approach for Document Retrieval. Appl. Sci. 2023, 13, 10285. Sidi, M.L.; Gunal, S. A Purely Entity-Based Semantic Search Approach for Document Retrieval. Appl. Sci. 2023, 13, 10285.
Abstract
Over the past decade, Knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purpose, understanding how effectively lev-erage entity-based representation still needs to be resolved. This paper proposes a Purely Enti-ty-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method, (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method, for queries whose all terms are annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.000). Thus, using our approach with any other document retrieval method would be an added value, unless that method achieves the maximum nDCG@5 score for those highly annotated queries.
Keywords
Keywords: Information Retrieval; Document Retrieval; Knowledge graphs; Entity-based Search; Entity Linking.
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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