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, 21-35

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

SEKTbar