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Query Expansion Using DBpedia and WordNet

Published: 07 March 2019 Publication History

Abstract

This paper presents a semantic approach based on DBpedia and WordNet to extract the candidate terms for query expansion. We divided DBpedia features into: general features and specific features that are available depending on the domain of the resource. We evaluated our approach using 44 queries from TREC AP88-90, and discovered that the results are improved when using the hybrid approach that combines WordNet and DBpedia, versus using only WordNet for short and long queries. The comparison of our approach with literature technique that used DBpedia for query expansion showed an improvement in P@20 for both short and long queries.

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Cited By

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  • (2022) Retracted: QGMS : A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence Computational Intelligence10.1111/coin.1251440:1Online publication date: 8-Mar-2022
  • (2022)Metadata Driven Semantically Aware Medical Query ExpansionKnowledge Graphs and Semantic Web10.1007/978-3-030-91305-2_17(223-233)Online publication date: 1-Jan-2022
  • (2020)Enhancing information retrieval performance by using social analysisSocial Network Analysis and Mining10.1007/s13278-020-00635-w10:1Online publication date: 8-Apr-2020

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cover image ACM Other conferences
ArabWIC 2019: Proceedings of the ArabWIC 6th Annual International Conference Research Track
March 2019
136 pages
ISBN:9781450360890
DOI:10.1145/3333165
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Google Inc.
  • Microsoft: Microsoft
  • Facebook: Facebook
  • ORACLE: ORACLE
  • IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2019

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Author Tags

  1. DBpedia
  2. Information Retrieval
  3. Query Expansion
  4. WordNet

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  • Refereed limited

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ArabWIC 2019

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ArabWIC 2019 Paper Acceptance Rate 20 of 36 submissions, 56%;
Overall Acceptance Rate 20 of 36 submissions, 56%

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Cited By

View all
  • (2022) Retracted: QGMS : A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence Computational Intelligence10.1111/coin.1251440:1Online publication date: 8-Mar-2022
  • (2022)Metadata Driven Semantically Aware Medical Query ExpansionKnowledge Graphs and Semantic Web10.1007/978-3-030-91305-2_17(223-233)Online publication date: 1-Jan-2022
  • (2020)Enhancing information retrieval performance by using social analysisSocial Network Analysis and Mining10.1007/s13278-020-00635-w10:1Online publication date: 8-Apr-2020

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