P. Clerkin, P. Cunningham, C. Hayes, Ontology discovery for the Semantic Web using hierarchical clustering, in: Semantic Web Mining Workshop, 2001. Home/Search Document Details and Download
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A Road map to More Effective Web Personalization: Integrating.. - Dai, Mobasher (2003) (3 citations) (Correct)
....web sites, for instance, usually have well structured Web content, including predefined metadata or database schema. Therefore it is easier to build automatic ontology extraction mechanisms that are site specific. There have been a number of e#orts dealing with the ontology learning problem [4, 8, 11, 2]. A wide range of information, such as thesauri, content features, and database schema can help to identify ontologies. Many of these approaches have focused on extracting ontological information from the Web, in general. In [1] the notion of Semantic Web Mining was introduced, including a ....
P. Clerkin, P. Cunningham, and C. Hayes. Ontology discovery for the semantic web using hierarchical clustering. In Semantic Web Mining Workshop at ECML/PKDD-2001.
Semantic Enhancement Engine: A Modular Document.. - Hammond, Sheth, Kochut (2002) (3 citations) (Correct)
....Knowledgebase. As with the specification for ontologies, the WorldModel and Knowledgebase definition process involves domain specific expertise as well as an understanding of eventual application requirements. While some clustering techniques can provide initial input to semi automate this process [2, 3], it cannot generally be completely automated if high quality results are needed. Also the techniques that rely on learning from structured data [3] are not particularly practical. The Knowledgebase reflects the subset of the real world for which a semantic application is created. As such, it is ....
....as an understanding of eventual application requirements. While some clustering techniques can provide initial input to semi automate this process [2, 3] it cannot generally be completely automated if high quality results are needed. Also the techniques that rely on learning from structured data [3] are not particularly practical. The Knowledgebase reflects the subset of the real world for which a semantic application is created. As such, it is an important part of the solution. It enables the extraction of value added semantic metadata, such as the ticker symbol INTC when a document only ....
P. Clerkin, P. Cunningham, C. Hayes, "Ontology Discovery for the Semantic Web Using Hierarchical Clustering", Semantic Web Mining Workshop at ECML/PKDD-2001.
Web Cooperativa (Trabajo de Investigación) - Gayo-Avello (2002) (Correct)
....Y. y Yun, Y. 1995) An adaptive agent for automated web browsing , Journal of Visual Communication and Image Representation, Vol. 6, No. 4. 9] Balabanovic, M. y Shoham, Y. 1997) Fab: Content Based, Collaborative Recommendation , Communications of the ACM, Vol. 40, No. 3, pp. 66 72. [10] Berners Lee, T. 1989) Information Management: A Proposal , http: www.w3.org History 1989 proposal.html (accedido 7 6 2002) 11] Berners Lee, T. 1998) Semantic Web Road map , http: www.w3.org DesignIssues Semantic.html (accedido 14 6 2002) 12] Berners Lee, T. Hendler, J. Lassila, ....
....useful semantic information out of the reach of the Semantic Web. Summarizing, a Semantic Web application requires an ontology that describes the fundamental concepts of a particular field in order to semantically marlcup the documents. Obviously, the ontologies can be generated semi automatically [10][11] as well as the documents semantic marlcup [12] However, there are situations in which this is very difficult to apply. For example, it may be the case that building the ontology is not easy or possible [13] especially in the case of free rex0, or that there is no economic interest, or ....
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P. Clerkin, P. Cunningham, and C. Hayes, "Ontology Discovery for the Semantic Web Using Hierarchical Clustering," Semantic Web Mining Workshop, 2001.
Modeling User Interests By Conceptual Clustering - Godoy, Amandi (2005) (Correct)
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P. Clerkin, P. Cunningham, C. Hayes, Ontology discovery for the Semantic Web using hierarchical clustering, in: Semantic Web Mining Workshop, 2001.
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