Abstract
In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.
Highlights
The classical Information Retrieval Systems (IRSs) mainly use words to represent the content of documents and queries, the matching is carried out by using these words on lexical level rather than on semantic level, in other words, when the IR system takes a query, it retrieves documents that contain the query words without considering the semantics behind them
This paper presents a new hybrid approach of query expansion (QE) for Arabic Information Retrieval (IR); in which the authors demonstrate how the Pseudo Relevance Feedback (PRF) expansion can be combined to an external resource such as the Arabic WordNet (AWN) to enhance Query expansion (QE) process, in selection and weighting of expansion terms
For better understanding the effectiveness of the approach, these results are described by percentage comparisons (%), into two categories; retrieving by concept-based query reformulation compared to baseline retrieving and retrieving by query reformulation based on the semantic tree compared to baseline retrieving, as shown in Table
Summary
The classical Information Retrieval Systems (IRSs) mainly use words to represent the content of documents and queries, the matching is carried out by using these words on lexical level rather than on semantic level, in other words, when the IR system takes a query, it retrieves documents that contain the query words without considering the semantics behind them. Query expansion (QE) is one of the proposed approaches used to solve problems mentioned above, based on the following principle; more the number of keywords in the query is greater, more the information need is well described, since it certainly includes a greater number of index keywords that represent relevant documents (Xu & Croft, 2000). It consists of adding new words or terms into the original query in order to improve the retrieval performance
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