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The ever growing amount of Semantic Web data has made it increasingly difficult to analyse the information required by the users. Association rule mining is one of the most useful techniques for discovering frequent patterns among RDF triples. In this context, some statistical methods strongly rely on the user intervention that is time-consuming and error-prone due to a large amount of data. In these studies, the rule quality factors (e.g. Support and Confidence measures) consider only knowledge in the instance-level data. However, Semantic Web data contains knowledge in both instance-level and schema-level. In this paper, we introduce an approach called SWARM (Semantic Web Association Rule Mining) to automatically mine Semantic Association Rules from RDF data. We discuss how to utilize knowledge encode in the schema-level to enrich the semantics of rules. We also show that our approach is able to reveal common behavioral patterns associated with knowledge in the instance-level and schema-level. The proposed rule quality factors (Support and Confidence) consider knowledge not only in the instance-level but also schema-level. Experiments performed on the DBpedia Dataset (3.8) demonstrate the usefulness of the proposed approach.

Item Type:	Refereed Conference Paper
Keywords:	Semantic Web data, association rule mining, ontology, knowledge discovery
Research Division:	Information and Computing Sciences
Research Group:	Computer vision and multimedia computation
Research Field:	Pattern recognition
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Application software packages
UTAS Author:	Bai, Q (Dr Quan Bai)
ID Code:	140686
Year Published:	2016
Web of Science® Times Cited:	9
Deposited By:	Information and Communication Technology
Deposited On:	2020-09-01
Last Modified:	2020-11-09
Downloads:	25 View Download Statistics