Ontology-based personalised retrieval in support of reminiscence

Lei Shi, Rossi Setchi

Research output: Contribution to journalArticlepeer-review

16 Citations (SciVal)


This research proposes a knowledge-based framework for integrating ontology-based personalised retrieval and reminiscence support. The aim is to assist people in recalling, browsing and re-discovering events from their lives by considering their profiles and background knowledge and providing them with customised information retrieval. To model a user’s background knowledge, this paper defines a user profile space (UPS) model and describes its construction method. The model has a dynamic structure based on relevance feedback and interactions with users. Furthermore, this work introduces a multi-ontology query expansion model which uses user-oriented ontologies, UPSs and semantic feature-selection algorithms to expand queries. In this model, knowledge-spanning trees are generated from ontology/UPS graphs based on the queries. These knowledge-spanning trees contain semantic features which enhance the representations of the original queries and further facilitate personalised retrieval on a semantic basis. The experimental results indicate that the proposed approach consistently outperforms term-based retrieval on precision, recall and f-score, which proves the positive effect of using ontology/user profile spaces in query expansion and personalised retrieval.
Original languageEnglish
Pages (from-to)47-61
JournalKnowledge-Based Systems
Publication statusPublished - Jun 2013


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