A Taxonomy and Survey of Semantic Approaches for Query Expansion
Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic app...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8625452/ |
| _version_ | 1819170099740803072 |
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| author | Muhammad Ahsan Raza Rahmah Mokhtar Noraziah Ahmad Maruf Pasha Urooj Pasha |
| author_facet | Muhammad Ahsan Raza Rahmah Mokhtar Noraziah Ahmad Maruf Pasha Urooj Pasha |
| author_sort | Muhammad Ahsan Raza |
| collection | DOAJ |
| description | Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This paper surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods, and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation, and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested. |
| first_indexed | 2024-12-22T19:30:01Z |
| format | Article |
| id | doaj.art-25282b35fc104df68a2100d93b8c0881 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:30:01Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-25282b35fc104df68a2100d93b8c08812022-12-21T18:15:07ZengIEEEIEEE Access2169-35362019-01-017178231783310.1109/ACCESS.2019.28946798625452A Taxonomy and Survey of Semantic Approaches for Query ExpansionMuhammad Ahsan Raza0https://orcid.org/0000-0003-2299-4440Rahmah Mokhtar1Noraziah Ahmad2Maruf Pasha3https://orcid.org/0000-0002-1286-5701Urooj Pasha4Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Kuantan, MalaysiaFaculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Kuantan, MalaysiaFaculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Kuantan, MalaysiaDepartment of Information Technology, Bahauddin Zakariya University, Multan, PakistanInstitute of Management Sciences, Bahauddin Zakariya University, Multan, PakistanConventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This paper surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods, and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation, and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested.https://ieeexplore.ieee.org/document/8625452/Information retrievalmorphological expansionontologysemantic query expansion |
| spellingShingle | Muhammad Ahsan Raza Rahmah Mokhtar Noraziah Ahmad Maruf Pasha Urooj Pasha A Taxonomy and Survey of Semantic Approaches for Query Expansion IEEE Access Information retrieval morphological expansion ontology semantic query expansion |
| title | A Taxonomy and Survey of Semantic Approaches for Query Expansion |
| title_full | A Taxonomy and Survey of Semantic Approaches for Query Expansion |
| title_fullStr | A Taxonomy and Survey of Semantic Approaches for Query Expansion |
| title_full_unstemmed | A Taxonomy and Survey of Semantic Approaches for Query Expansion |
| title_short | A Taxonomy and Survey of Semantic Approaches for Query Expansion |
| title_sort | taxonomy and survey of semantic approaches for query expansion |
| topic | Information retrieval morphological expansion ontology semantic query expansion |
| url | https://ieeexplore.ieee.org/document/8625452/ |
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