Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An Introduction to Neural Networks
An Introduction to Neural Networks
Self-Organizing Maps
Text Mining Techniques to Automatically Enrich a Domain Ontology
Applied Intelligence
Mining Significant Associations in Large Scale Text Corpora
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Integrating word relationships into language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Mining semantically related terms from biomedical literature
ACM Transactions on Asian Language Information Processing (TALIP)
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Mining Generalized Associations of Semantic Relations from Textual Web Content
IEEE Transactions on Knowledge and Data Engineering
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
Modeling term associations for ad-hoc retrieval performance within language modeling framework
ECIR'07 Proceedings of the 29th European conference on IR research
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The paper introduces a web-based eHealth platform currently being developed that will assist patients with certain chronic diseases. The ultimate aim is behavioral change. This is supported by online assessment and feedback which visualizes actual behavior in relation to target behavior. Disease-specific information is provided through an information portal that utilizes lightweight ontologies associative networks in combination with text mining. The paper argues that classical word-based information retrieval is often not sufficient for providing patients with relevant information, but that their information needs are better addressed by concept-based retrieval. The focus of the paper is on the semantic retrieval component and the learning of a lightweight ontology from text documents, which is achieved by using a biologically inspired neural network. The paper concludes with preliminary results of the evaluation of the proposed approach in comparison with traditional approaches.