A hybrid semantic query expansion approach for Arabic information retrieval

In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). T...

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Vydané v:	Journal of big data Ročník 7; číslo 1; s. 1 - 19
Hlavní autori:	ALMarwi, Hiba, Ghurab, Mossa, Al-Baltah, Ibrahim
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 29.06.2020 Springer Nature B.V SpringerOpen
Predmet:	Accuracy Advanced Soft Computing Methodologies and Applications in Social Media Big Data Analytics Arabic language Big Data Communications Engineering Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Database Management Dependency Function words Information retrieval Information Storage and Retrieval Keywords Knowledge base Knowledge bases (artificial intelligence) Mathematical Applications in Computer Science Meaning Networks Optimization Particle swarm optimization Prototypes Queries Query expansion Researchers Retrieval Semantics Syntactic processing Term frequency Weighting Weighting methods Word embeddings Word meaning WordNet Query expansion Information retrieval Particle swarm optimization Term frequency Word embeddings WordNet
ISSN:	2196-1115, 2196-1115
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Abstract	In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). There are several approaches in query expansion field such as statistical approach. This approach depends on term frequency to generate expansion features; nevertheless it does not consider meaning or term dependency. In addition, there are other approaches such as semantic approach which depends on a knowledge base that has a limited number of terms and relations. In this paper, researchers propose a hybrid approach for query expansion which utilizes both statistical and semantic approach. To select the optimal terms for query expansion, researchers propose an effective weighting method based on particle swarm optimization (PSO). A system prototype was implemented as a proof-of-concept, and its accuracy was evaluated. The experimental was carried out based on real dataset. The experimental results confirm that the proposed approach enhances the accuracy of query expansion.
AbstractList	In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). There are several approaches in query expansion field such as statistical approach. This approach depends on term frequency to generate expansion features; nevertheless it does not consider meaning or term dependency. In addition, there are other approaches such as semantic approach which depends on a knowledge base that has a limited number of terms and relations. In this paper, researchers propose a hybrid approach for query expansion which utilizes both statistical and semantic approach. To select the optimal terms for query expansion, researchers propose an effective weighting method based on particle swarm optimization (PSO). A system prototype was implemented as a proof-of-concept, and its accuracy was evaluated. The experimental was carried out based on real dataset. The experimental results confirm that the proposed approach enhances the accuracy of query expansion. Abstract In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have similar meaning with syntactical different keywords (form). One of the well-known approaches to overcome this limitation is query expansion (QE). There are several approaches in query expansion field such as statistical approach. This approach depends on term frequency to generate expansion features; nevertheless it does not consider meaning or term dependency. In addition, there are other approaches such as semantic approach which depends on a knowledge base that has a limited number of terms and relations. In this paper, researchers propose a hybrid approach for query expansion which utilizes both statistical and semantic approach. To select the optimal terms for query expansion, researchers propose an effective weighting method based on particle swarm optimization (PSO). A system prototype was implemented as a proof-of-concept, and its accuracy was evaluated. The experimental was carried out based on real dataset. The experimental results confirm that the proposed approach enhances the accuracy of query expansion.
ArticleNumber	39
Author	ALMarwi, Hiba Al-Baltah, Ibrahim Ghurab, Mossa
Author_xml	– sequence: 1 givenname: Hiba surname: ALMarwi fullname: ALMarwi, Hiba email: Hebh.Almarwi@gmail.com organization: Computer Science Department, Sanaa University – sequence: 2 givenname: Mossa surname: Ghurab fullname: Ghurab, Mossa organization: Computer Science Department, Sanaa University – sequence: 3 givenname: Ibrahim surname: Al-Baltah fullname: Al-Baltah, Ibrahim organization: Information Technology Department, Sanaa University
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Cites_doi	10.1177/0165551515594722 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 10.1145/2789210 10.1147/rd.22.0159 10.1016/j.ipm.2007.07.015 10.1145/2071389.2071390 10.1109/4235.985692 10.1145/32206.32212 10.7763/IJFCC.2013.V2.229 10.1145/775047.775110 10.1609/aaai.v30i1.10351 10.1007/978-3-642-32891-6_22 10.1007/978-1-4471-2099-5_7 10.1007/11546924_17 10.1145/1031171.1031181 10.1145/2786805.2786855 10.1109/HUMANCOM.2010.5563333 10.1007/978-3-7091-2490-1_12 10.3115/v1/D14-1162 10.1145/1277741.1277747 10.1109/ISME.2010.79 10.1109/ICCES.2006.320487 10.1109/ISSP.2013.6526942 10.3115/v1/P15-1025 10.1109/ICADIWT.2009.5273835 10.1007/978-3-319-26453-0_6 10.18653/v1/P16-1035 10.1109/GRC.2007.4403184 10.1109/CIST.2012.6388065 10.1109/FUZZ-IEEE.2017.8015482 10.1007/978-3-540-31865-1_15
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Snippet	In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that have... Abstract In fact, most of information retrieval systems retrieve documents based on keywords matching, which are certainly fail at retrieving documents that...
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SubjectTerms	Accuracy Advanced Soft Computing Methodologies and Applications in Social Media Big Data Analytics Arabic language Big Data Communications Engineering Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Database Management Dependency Function words Information retrieval Information Storage and Retrieval Keywords Knowledge base Knowledge bases (artificial intelligence) Mathematical Applications in Computer Science Meaning Networks Optimization Particle swarm optimization Prototypes Queries Query expansion Researchers Retrieval Semantics Syntactic processing Term frequency Weighting Weighting methods Word embeddings Word meaning WordNet
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Title	A hybrid semantic query expansion approach for Arabic information retrieval
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Volume	7
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