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Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval.

Ram Kumar1, S C Sharma1

  • 1Electronics and Computer Discipline, DPT, Indian Institute of Technology, Roorkee, India.

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Summary
This summary is machine-generated.

This study introduces an Improved Aquila Optimization-based COOT (IAOCOOT) algorithm for query expansion, enhancing information retrieval by analyzing diverse data sources and using a Modified Needleman Wunsch algorithm to improve semantic matching.

Keywords:
Aquila optimizationCOOT optimizationInformation retrieval systemModified Needleman WunschQuery expansionSemantic information retrieval

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Area of Science:

  • Information Retrieval
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Query expansion is crucial for improving data retrieval efficiency but struggles with specific query types like phrase and individual queries.
  • Existing methods often use uniform data sources and weighting, failing to capture comprehensive term relationships and leading to suboptimal results.
  • Semantic heterogeneity and inaccurate word similarity evaluation hinder relevance matching in document retrieval.

Purpose of the Study:

  • To develop a novel query expansion approach addressing limitations of current techniques, particularly for phrase and individual queries.
  • To enhance the accuracy of semantic aspect retrieval and relevance matching in information retrieval systems.
  • To improve the handling of uncertainty, imprecision, and semantic ambiguity in the information retrieval process.

Main Methods:

  • A novel query expansion technique analyzing multiple data sources: WordNet, Wikipedia, and Text REtrieval Conference (TREC).
  • Implementation of an Improved Aquila Optimization-based COOT (IAOCOOT) algorithm for retrieving semantically relevant aspects.
  • Utilization of the Modified Needleman Wunsch algorithm to address uncertainty, imprecision, and semantic ambiguity.
  • Employing a top-k words selection technique to identify and return the k most similar words.

Main Results:

  • The IAOCOOT model demonstrates improved performance in retrieving semantically matching aspects for query expansion.
  • The integration of diverse data sources and advanced algorithms addresses limitations in capturing comprehensive term relationships.
  • The Modified Needleman Wunsch algorithm effectively mitigates issues related to uncertainty and semantic ambiguity.

Conclusions:

  • The proposed IAOCOOT model offers a significant advancement in query expansion techniques, particularly for challenging query types.
  • The approach effectively enhances relevance matching by accurately evaluating word similarity and semantic aspects.
  • The study validates the proposed model's effectiveness using standard Information Retrieval performance metrics against state-of-the-art methods.