Ontology-based user profile learning
Ontology-based user profile learning
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DOI：
10.1007/s10489-011-0301-4
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发表时间：
2012-06
影响因子：
5.3
通讯作者：
Victoria Eyharabide;A. Amandi
中科院分区：
文献类型：
--
作者：
Victoria Eyharabide;A. Amandi
个人代理收集有关用户配置文件中用户的信息。在这项工作中，我们提出了一种新的基于本体的用户档案学习方法。特别是，我们的目标是使用数据挖掘技术和本体来学习上下文丰富的用户配置文件。我们感兴趣的是知道数据挖掘技术可以在多大程度上用于用户简档生成，以及如何利用本体来改进用户简档。其目的是通过使用关联规则、贝叶斯网络和本体来使用上下文信息来丰富用户配置文件的语义，以提高代理的性能。在运行时，我们根据用户的行为观察了解与用户相关的上下文。然后，我们将学习到的相关上下文表示为本体片段。令人鼓舞的实验结果表明，将语义包括到用户配置文件中是有用的，以及使用本体集成代理和数据挖掘的优势。
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.