Article by Dunja Mladenic, Marko Grobelnik, Blaz Fortuna, and Miha Grcar, Chapter 3 in Semantic Knowledge Management: Integrating Ontology Management, Knowledge Discovery, and Human Language Technologies, Springer Verlag, Berlin, 2009, Summary of Knowledge Discovery for Semantic Web Summary by Andrew Zitzelberger

What is the Semantic Web? The Semantic Web can be seen as mainly dealing with the integration of many, already existing ideas and technologies with the specific focus of upgrading the existing nature of web-based information systems to a more “semantic” oriented nature.

What is Knowledge Discovery? Knowledge discovery can be defined as a process which aims at the extraction of interesting (non- trivial, implicit, previously unknown, and potentially useful) information from data in large databases.

How Does Knowledge Discovery Help Us? Ontology Construction  Domain understanding (what is the area we are dealing with?)  Information Retrieval  Data understanding (what is the available data and how is it related?)  Machine Learning and Data Mining  Task definition (what to do with the data ?)  Ontology population  Extending the ontology  Ontology learning (semi-automated process)  Ontology evaluation (estimate quality of solutions)  Gold Standards  Human refinement (iterate)

How Does Knowledge Discovery Help Us? Domain Knowledge  Capture domain specifics  Track user’s search interests Dynamic Data  How does data change over time?  Data drift and visualization of data changes Multimodal and Multilingual Data  Non-textual data  Pre-processing other forms of data into more useful representations

Tools OntoClassify  Used for ontology population OntoGen  Used to edit topic ontologies SEKTbar  Used to maintain dynamic user profiles  Creates an ontology to model the interests of the user in order to highlight items of expected interest on the pages the user is visiting.

SEKTbar