http://www.bibsonomy.org/burst/user/gromgull/machine-learningBibSonomy BuRST Feed for /user/gromgull/machine-learning2008-10-07T18:56:01+02:00http://www.bibsonomy.org/bibtex/2b8b9aa8dccb2ed33725b45aba9893bc2/gromgullgromgull2008-04-03T17:02:56+02:00ontologies smushing machine-learning <span style="color:#555555;">Chao <a href="http://www.bibsonomy.org/author/Wang">Wang</a> und Jie <a href="http://www.bibsonomy.org/author/Lu">Lu</a> und Guangquan <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> </span><em>Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on, </em><em>Seite536-539. </em>(<em>2006</em>)Thu Apr 03 17:02:56 CEST 2008Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on536-539Integration of Ontology Data through Learning Instance Matching2006ontologies smushing machine-learning Information integration with the aid of ontology can roughly be divided into two levels: schema level and data level. Most research has been focused on the schema level, i.e., mapping/matching concepts and properties in different ontologies with each other. However, the data level integration is equally important, especially in the decentralized semantic Web environment. Noticing that ontology data (in the form of instances of concepts) from different sources often have different perspectives and may overlap with each other, we develop a matching method that utilizes the features of ontology and employs the machine learning approach to integrate those instances. By exploring ontology features, this method performs better than other general methods, which is revealed in our experiments. Through the process that implements the matching method, ontology data can be integrated together to offer more sophisticated servicesKnowledge Discovery and Machine Learning Bonn (www.kdml-bonn.de)http://www.bibsonomy.org/bibtex/2a98a3c732acb64f777a364b89c118477/gromgullgromgull2008-03-10T00:01:09+01:00structured-data machine-learning kernel-methods <span style="color:#555555;">Thomas <a href="http://www.bibsonomy.org/author/G{\&#034;a}rtner">G&#228;rtner</a> </span><em>Universit&#228;t Bonn, </em>(<em>2005</em>)Mon Mar 10 00:01:09 CET 2008Kernels for Structured Data2005structured-data machine-learning kernel-methods dblphttp://www.bibsonomy.org/bibtex/250c95a6c9f327a61fdf3cdc570588530/gromgullgromgull2008-03-09T18:51:46+01:00machine-learning pattern-extraction graphs <span style="color:#555555;">Kenichi <a href="http://www.bibsonomy.org/author/Yoshida">Yoshida</a> und Hiroshi <a href="http://www.bibsonomy.org/author/Motoda">Motoda</a> </span><em>Artif. 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Newsl.</em><em>7(2):3--12</em>(<em>2005</em>)Sun Feb 17 13:59:59 CET 2008SIGKDD Explor. Newsl.23--12Link mining: a survey72005link-mining survey information-extraction machine-learning Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single--object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross--fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes.The big onehttp://www.bibsonomy.org/bibtex/2cb5b80b9836e0ffe69e9599f31646a49/gromgullgromgull2008-02-17T13:54:35+01:00machine-learning web-mining <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Dingli">Dingli</a> und F. <a href="http://www.bibsonomy.org/author/Ciravegna">Ciravegna</a> und Y. <a href="http://www.bibsonomy.org/author/Wilks">Wilks</a> </span><em>Workshop on Knowledge Markup and Semantic Annotation, </em>(<em>2003</em>)Sun Feb 17 13:54:35 CET 2008Workshop on Knowledge Markup and Semantic Annotation{Automatic Semantic Annotation using Unsupervised Information Extraction and Integration}2003machine-learning web-mining