140686 - SWARM.pdf (410.06 kB)
SWARM: An approach for mining semantic association rules from semantic web data
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.
History
Publication title
Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016). Lecture Notes in Computer Science, volume 9810Volume
9810Editors
R Booth and ML ZhangPagination
30-43ISBN
9783319429106Department/School
School of Information and Communication TechnologyPublisher
SpringerPlace of publication
SwitzerlandEvent title
14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016)Event Venue
Phuket, ThailandDate of Event (Start Date)
2016-08-22Date of Event (End Date)
2016-08-26Rights statement
Copyright 2016 SpringerRepository Status
- Open