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Ontology-based user profile learning

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Abstract

Personal agents gather information about users in a user profile. In this work, we propose a novel ontology-based user profile learning. Particularly, we aim to learn context-enriched user profiles using data mining techniques and ontologies. We are interested in knowing to what extent data mining techniques can be used for user profile generation, and how to utilize ontologies for user profile improvement. The objective is to semantically enrich a user profile with contextual information by using association rules, Bayesian networks and ontologies in order to improve agent performance. At runtime, we learn which the relevant contexts to the user are based on the user’s behavior observation. Then, we represent the relevant contexts learnt as ontology segments. The encouraging experimental results show the usefulness of including semantics into a user profile as well as the advantages of integrating agents and data mining using ontologies.

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Authors and Affiliations

  1. ISISTAN Research Institute, Computer Science Department, Unicen University, Tandil, Argentina

    Victoria Eyharabide & Analía Amandi

  2. CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina

    Victoria Eyharabide & Analía Amandi

Authors
  1. Victoria Eyharabide
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  2. Analía Amandi
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Correspondence to Victoria Eyharabide.

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Eyharabide, V., Amandi, A. Ontology-based user profile learning. Appl Intell 36, 857–869 (2012). https://doi.org/10.1007/s10489-011-0301-4

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  • DOI: https://doi.org/10.1007/s10489-011-0301-4

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