Workshop at ECML/PKDD-2001,
September 3, 2001, Freiburg, Germany

Home

Call for Papers

Accepted Papers

Online Proc.

Program

Committees

Print Me

The workshop aims to bring together researchers and practitioners from the two fastly developing research areas Semantic Web and Web Mining. One can improve the results of Web Mining by exploiting the new semantic structures in the web. Furthermore, Web Mining can help to build the Semantic Web.

The Semantic Web is based on a vision of Tim Berners-Lee. He suggests to enrich the web by machine processable information which is organized on different levels (see http://www.w3.org/DesignIssues/Semantic.html). For Web Mining, the levels from XML and RDF to ontologies and logics are of particular interest. Web Mining applies data mining techniques on the web. Three areas can be distinguished: Web usage mining analyzes the user behavior, web structure mining explores the hyperlink structure, and web content mining exploits the contents of the documents in the web.

In the workshop, we want to discuss the use of XML, RDF, ontologies, and logics for the three web mining areas; and the support of web mining techniques for building (XML and RDF) schemes and ontologies. There are quite a number of people from different communities that approach the field of Semantic Web Mining from different, interesting angles. The goal of the workshop is to establish communiction between these communities.

Topics of interest include, but are not limited to:

• Concept Taxonomies (e.g. product hierarchies) and Web Mining
• Using the Syntactic Layer (XML) for Semantic Web Mining
• Using the Vocabulary Layer (e.g. RDF-Schema) for Semantic Web Mining
• Using the Logical Layer (e.g. Description Logics/OIL) for Semantic Web Mining
• User Profiling, Segmentation and Interpretation with Background Knowledge
• Using Background Knowledge for Web Mining
• Metadata-based Text Mining
• Text Mining for Creating Metadata
• Learning Taxonomies and Ontologies from the Web
• Metadata Harvesting
• Information Extraction with Machine Learning