Ontology-driven personalized information retrieval for XML documents

TL;DR

This paper presents an ontology-driven framework that enhances XML document retrieval by integrating semantic resources and user profiles, resulting in more personalized and accurate search results.

Contribution

It introduces a novel method combining domain ontologies and user profiles to improve semantic matching and retrieval effectiveness for XML documents.

Findings

Higher precision and recall compared to keyword-based methods

Effective use of concept-weighting emphasizing specific concepts

Improved relevance and personalization in search results

Abstract

This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents. Traditional information retrieval systems (IRS) often overlook user-specific needs and return identical results for the same query, despite differences in users' knowledge, preferences, and objectives. We integrate external semantic resources, namely a domain ontology and user profiles, into the retrieval process. Documents, queries, and user profiles are represented as vectors of weighted concepts. The ontology applies a concept-weighting mechanism that emphasizes highly specific concepts, as lower-level nodes in the hierarchy provide more precise and targeted information. Relevance is assessed using semantic similarity measures that capture conceptual relationships beyond keyword matching, enabling personalized and fine-grained matching among user profiles, queries, and documents. Experimental results show that combining ontologies with user profiles improves retrieval effectiveness, achieving higher precision and recall than keyword-based approaches. Overall, the proposed framework enhances the relevance and adaptability of XML search results, supporting more user-centered retrieval.