Adding Semantics to Decision Tables: a New Approach to Data and Knowledge Engineering?

Yan Tang, Robert Meersman, Jan Vanthienen

Onderzoeksoutput: ChapterResearchpeer review


The study of decision tables has been investigated for more than fifty years. Research interests range largely from programming to control systems, robotics, database, and so forth. Modern ontology technologies provide a promising solution to the decision support reasoning and collaborative decision support. This article proposes a new approach to data and knowledge engineering using Semantic Decision Table (SDT), which is defined as a semantically rich decision table supported by Ontology Engineering (OE). An SDT is a decision table, which contains all the features of decision tables, such as correctness, completeness and syntactical consistence. In addition, this particular research provides a means to capture and examine decision makers\' concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner.
Many researchers have been trying to extend decision tables for different purposes in various fields since the last two decades. This chapter addresses fundamental discussions on SDT, including the comparison with different types of extensions to decision tables. In order to demonstrate the advantages of SDT, we will show an application of SDT, which supports ontology-based data matching in the domain of e-learning and training.
The motivation and challenges of this chapter is as follows:
* Demonstrate the advantages of SDT brought forward by the modern Semantic Web (SW) and OE technologies
* Explore possible applications of SDT and demonstrate its usefulness
* Illustrate how SDT can assist ontology-based data matching processes
Originele taal-2English
TitelApplied Semantic Web Technologies
RedacteurenVijayan Sugumaran, Jon Atle Gulla
UitgeverijCRC Press
ISBN van geprinte versie978-1-4398-0156-7
StatusPublished - 2012

Bibliografische nota

Vijayan Sugumaran and Jon Atle Gulla


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