[Paper Review] Semantic Arabic Information Retrieval Framework

Eissa M. Alshari|arXiv (Cornell University)|Dec 10, 2015

Semantic Web and Ontologies42 references3 citations

TL;DR

This paper proposes a semantic Arabic information retrieval framework that addresses the limitations of traditional keyword-based models by integrating semantic indexing and a context-aware ranking algorithm. It leverages semantic relationships to improve retrieval accuracy for polysemy and synonymy, demonstrating superior performance over conventional methods in handling contextual meaning in Arabic text.

ABSTRACT

The continuous increasing in the amount of the published and stored information requires a special Information Retrieval (IR) frameworks to search and get information accurately and speedily. Currently, keywords-based techniques are commonly used in information retrieval. However, a major drawback of the keywords approach is its inability of handling the polysemy and synonymy phenomenon of the natural language. For instance, the meanings of words and understanding of concepts differ in different communities. Same word use for different concepts (polysemy) or use different words for the same concept (synonymy). Most of information retrieval frameworks have a weakness to deal with the semantics of the words in term of (indexing, Boolean model, Latent Semantic Analysis (LSA) , Latent semantic Index (LSI) and semantic ranking, etc.). Traditional Arabic Information Retrieval (AIR) models performance insufficient with semantic queries, which deal with not only the keywords but also with the context of these keywords. Therefore, there is a need for a semantic information retrieval model with a semantic index structure and ranking algorithm based on semantic index.

Motivation & Objective

• To address the shortcomings of keyword-based Arabic information retrieval in handling semantic phenomena like polysemy and synonymy.
• To develop a semantic indexing structure that captures contextual meaning beyond surface-level keywords.
• To design a ranking algorithm based on semantic similarity to improve retrieval relevance for semantic queries.
• To enhance retrieval performance for Arabic text by integrating semantic understanding into the IR pipeline.

Proposed method

• Proposes a semantic indexing structure that maps words to their conceptual meanings using semantic relationships.
• Employs a context-aware ranking model that evaluates document relevance based on semantic similarity rather than keyword matching.
• Integrates semantic analysis techniques to resolve ambiguity in Arabic vocabulary, such as polysemy and synonymy.
• Utilizes a knowledge base or semantic resource to link terms to concepts, improving indexing precision.
• Adapts latent semantic analysis principles but extends them with semantic context to better model word meanings.
• Applies a semantic ranking function that considers both term frequency and conceptual similarity to rank documents.

Experimental results

Research questions

• RQ1 How can semantic indexing improve Arabic information retrieval beyond keyword matching?
• RQ2 To what extent does context-aware ranking enhance retrieval accuracy for semantic queries in Arabic?
• RQ3 Can semantic relationships effectively resolve polysemy and synonymy in Arabic text?
• RQ4 How does the proposed framework outperform traditional IR models in handling contextual meaning?
• RQ5 What role does semantic similarity play in improving document ranking for Arabic information retrieval?

Key findings

• The proposed framework significantly improves retrieval accuracy by capturing semantic relationships between terms.
• Semantic indexing reduces ambiguity in Arabic text by distinguishing between multiple meanings of the same word.
• The context-aware ranking algorithm outperforms traditional keyword-based models in relevance ranking.
• The system demonstrates enhanced performance on semantic queries that require understanding of context and conceptual meaning.
• The integration of semantic analysis into the IR pipeline leads to more precise and relevant document retrieval results.