Sat, 05 Jul 2008 05:32:14 BST CiteULike: brusilovsky's concept-extraction http://www.citeulike.org/user/brusilovsky/tag/concept-extraction CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/brusilovsky/article/1445552 <i>User Modeling and User-Adapted Interaction, Vol. 17, No. 3. (July 2007), pp. 217-255.</i> A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation Degemmis Marco Lops Pasquale Semeraro Giovanni doi:10.1007/s11257-006-9023-4 User Modeling and User-Adapted Interaction, Vol. 17, No. 3. (July 2007), pp. 217-255. 2007-07-10T06:33:10-00:00 2007 User Modeling and User-Adapted Interaction 0924-1868 17 3 217 255 Springer collaborative-filtering concept-extraction concept-map dlpaws japaws jlpaws pbblog recommender http://www.citeulike.org/user/brusilovsky/article/848978 <i>Intelligent Systems, IEEE [see also IEEE Intelligent Systems and Their Applications], Vol. 18, No. 1. (2003), pp. 14-21.</i><br /><br />To bring the Semantic Web to life and provide advanced knowledge services, we need efficient ways to access and extract knowledge from Web documents. Although Web page annotations could facilitate such knowledge gathering, annotations are rare and will probably never be rich or detailed enough to cover all the knowledge these documents contain. Manual annotation is impractical and unscalable, and automatic annotation tools remain largely undeveloped. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. An ontology uses concepts and relations to classify domain knowledge. Other researchers have used ontologies to support knowledge extraction, but few have explored their full potential in this domain. The paper considers the Artequakt project which links a knowledge extraction tool with an ontology to achieve continuous knowledge support and guide information extraction. The extraction tool searches online documents and extracts knowledge that matches the given classification structure. It provides this knowledge in a machine-readable format that will be automatically maintained in a knowledge base (KB). Knowledge extraction is further enhanced using a lexicon-based term expansion mechanism that provides extended ontology terminology. Automatic ontology-based knowledge extraction from Web documents H Alani Sanghee Kim DE Millard MJ Weal W Hall PH Lewis NR Shadbolt Intelligent Systems, IEEE [see also IEEE Intelligent Systems and Their Applications], Vol. 18, No. 1. (2003), pp. 14-21. 2006-09-18T14:47:09-00:00 2003 Intelligent Systems, IEEE [see also IEEE Intelligent Systems and Their Applications] 18 1 14 21 concept-extraction dlpaws ontology sspaws http://www.citeulike.org/user/brusilovsky/article/1847616 <i>(July 2006)</i><br /><br />When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle - they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence "Wal-Mart supply chain goes real time", how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that "Ciprofloxacin belongs to the quinolones group", how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge - an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets. Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge Evgeniy Gabrilovich Shaul Markovitch (July 2006) 2007-10-31T15:44:18-00:00 2006 categorization concept-extraction pbblog wikipedia http://www.citeulike.org/user/brusilovsky/article/1717603 <i>(2006), pp. 309-312.</i><br /><br />The task of researching information on a particular topic using the Web is mainly accomplished by using keyword-based search engines. Although this approach provides a good starting point, it remains a tedious task to collect additional information that puts this topic in greater context. In this paper we present ConceptWorld, an instrument to automatically discover various facets of a topic of interest by extracting concepts from Web documents. The result materializes as a network of semantic concepts with their various contextual interrelations and provides a holistic view on the topic of interest. Automated Concept Discovery from Web Resources Michael Dittenbach Helmut Berger Dieter Merkl doi:10.1109/WI.2006.45 (2006), pp. 309-312. 2007-10-02T00:20:58-00:00 2006 309 312 IEEE Computer Society concept-extraction