Conceptual User Tracking

Web usage mining applies data mining techniques to records of Web site visits. To better understand patterns of usage, analysis should take the semantics of visited URLs into account. This paper presents a framework for enhancing Web usage records with formal semantics based on an ontology underlying the site. Besides, it elicits automated methods of mapping URLs to application events. Using the ontology's taxonomy, we describe user actions at different levels of abstractions. Using the ontology's concepts and relations, we capture the multitude of user interests expressed by a visit to one page. We employ our ideas in an application of SEAL, a framework for semantic portals that uses Semantic Web technologies to support communities of interest. Different realizations of semantically enriched user tracking are discussed and related to other approaches. We describe first results from a prototypical system, and discuss benefits of Conceptual User Tracking forWeb usage mining.

[1]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[2]  Myra Spiliopoulou,et al.  Modelling and Mining Web Site Usage Strategies , 2002 .

[3]  Dan Brickley,et al.  Resource Description Framework (RDF) Model and Syntax Specification , 2002 .

[4]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[5]  Michael Kifer,et al.  F-logic: a higher-order language for reasoning about objects, inheritance, and scheme , 1989, SIGMOD '89.

[6]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[7]  Bettina Berendt,et al.  Using Site Semantics to Analyze, Visualize, and Support Navigation , 2004, Data Mining and Knowledge Discovery.

[8]  Jaideep Srivastava,et al.  Discovery of Interesting Usage Patterns from Web Data , 1999, WEBKDD.

[9]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[10]  Bettina Berendt,et al.  Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences , 2001, WEBKDD.

[11]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

[12]  Jaideep Srivastava,et al.  Web usage mining: discovery and application of interesting patterns from web data , 2000 .

[13]  Asunción Gómez-Pérez,et al.  (KA)2: building ontologies for the Internet: a mid-term report , 1999, Int. J. Hum. Comput. Stud..

[14]  Myra Spiliopoulou,et al.  Analysis of navigation behaviour in web sites integrating multiple information systems , 2000, The VLDB Journal.

[15]  Andreas Hotho,et al.  Conceptual Clustering of Text Clusters , 2003 .

[16]  Dieter Fensel,et al.  Ontobroker: Ontology Based Access to Distributed and Semi-Structured Information , 1999, DS-8.

[17]  Tao Luo,et al.  Integrating Web Usage and Content Mining for More Effective Personalization , 2000, EC-Web.

[18]  LinWeiyang,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2002 .

[19]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[20]  Alexander Maedche,et al.  Clustering Ontology-Based Metadata in the Semantic Web , 2002, PKDD.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[22]  Andreas Hotho,et al.  Towards Semantic Web Mining , 2002, SEMWEB.

[23]  Bamshad Mobasher,et al.  Using Ontologies to Discover Domain-Level Web Usage Profiles , 2002 .

[24]  Daniel Oberle,et al.  Semantic Community Web Portals-Personalization , 2003 .

[25]  Hiroki Kato,et al.  Navigation Analysis Tool based on the Correlation be- tween Contents Distribution and Access Patterns , 2000 .

[26]  Steffen Staab,et al.  SEAL - Tying Up Information Integration and Web Site Management by Ontologies , 2002, IEEE Data Eng. Bull..