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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Bibliographic Details
Published in:Journal of big data Vol. 7; no. 1; pp. 1 - 19
Main Authors: ALMarwi, Hiba, Ghurab, Mossa, Al-Baltah, Ibrahim
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 29.06.2020
Springer Nature B.V
SpringerOpen
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-020-00310-z