# Ontology-driven personalized information retrieval for XML documents

arXiv: https://arxiv.org/abs/2603.21139 · HTML: https://mlsanity.com/paper/2603.21139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Information Retrieval: Recent Advances and Beyond](https://mlsanity.com/paper/2301.08801/) (2023)
- [User Modeling and User Profiling: A Comprehensive Survey](https://mlsanity.com/paper/2402.09660/) (2024)
- [Modeling User Behavior with Graph Convolution for Personalized Product Search](https://mlsanity.com/paper/2202.06081/) (2022)
- [Multi-Topic Multi-Document Summarizer](https://mlsanity.com/paper/1401.0640/) (2014)
- [Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques](https://mlsanity.com/paper/1912.05530/) (2019)
- [Multivariate Representation Learning for Information Retrieval](https://mlsanity.com/paper/2304.14522/) (2023)
- [Towards an Ontology based integrated Framework for Semantic Web](https://mlsanity.com/paper/1305.7058/) (2013)
- [Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes](https://mlsanity.com/paper/2507.01810/) (2025)

## Abstract (arXiv)

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.