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Finding knowledge - or meaning - in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data ("Web mining"). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
List of contents
EWMF Papers.- A Website Mining Model Centered on User Queries.- WordNet-Based Word Sense Disambiguation for Learning User Profiles.- Visibility Analysis on the Web Using Co-visibilities and Semantic Networks.- Link-Local Features for Hypertext Classification.- Information Retrieval in Trust-Enhanced Document Networks.- Semi-automatic Creation and Maintenance of Web Resources with webTopic.- KDO Papers on KDD for Ontology.- Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis.- Semi-automatic Construction of Topic Ontologies.- Evaluation of Ontology Enhancement Tools.- KDO Papers on Ontology for KDD.- Introducing Semantics in Web Personalization: The Role of Ontologies.- Ontology-Enhanced Association Mining.- Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns.
About the author
Prof. Dr. Andreas Hotho ist seit Dezember 2009 Professor an der Universität Würzburg und leitet die Forschungsgruppe für Data Mining und Information Retrieval an der Fakultät für Mathematik und Informatik. Seit 2011 ist er Mitglied des L3S. Er studierte bis 1998 Wirtschaftsinformatik an der Technischen Universität Braunschweig. Von 1999 bis 2004 war er wissenschaftlicher Mitarbeiter am Institut für Angewandte Informatik und Formale Beschreibungsverfahren an der Universität Karlsruhe. Dort promovierte er im Bereich Text Mining, Data Mining und Semantic Web und wendete diese Methoden auch zur Kundensegmentierung bei der Deutschen Telekom AG an. Von April 2004 bis Dezember 2009 war er wissenschaftlicher Assistent an der Universität Kassel und beschäftigte sich dort u.a. mit den Themen Semantic Web Mining, Ontology Learning und Web 2.0 im speziellen Social-Bookmarking- und Tagging-Systeme. Seit Ende 2005 leitet er die Entwicklung des bekannten Publikationsverwaltungssystems BibSonomy. Aktuell forscht er im Bereich Web Science mit Fokus auf der Analyse von Daten aus sozialen Netzen und Sensor Daten, die in ubiquitären Systemen in Kombination mit Nutzerinformationen entstehen. In der Vergangenheit organisierte er verschiedene Workshops auf der Schnittstelle zwischen Semantic Web, Web 2.0 und Data Mining, häufig in Verbindung mit den Tagungen ECML PKDD, KDD und ESWC.
Summary
Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.
