Affiliation:
1. Università degli Studi di Bari “Aldo Moro”, Italy
Abstract
Onto-Relational Learning is an extension of Relational Learning aimed at accounting for ontologies in a clear, well-founded and elegant manner. The system -QuIn supports a variant of the frequent pattern discovery task by following the Onto-Relational Learning approach. It takes taxonomic ontologies into account during the discovery process and produces descriptions of a given relational database at multiple granularity levels. The functionalities of the system are illustrated by means of examples taken from a Semantic Web Mining case study concerning the analysis of relational data extracted from the on-line CIA World Fact Book.
Subject
Computer Networks and Communications,Information Systems
Reference48 articles.
1. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 207-216). New York, NY: ACM Press.
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). San Francisco, CA: Morgan Kaufmann.
3. Assmann, U., Henriksson, J., & Maluszynski, J. (2006). Combining safe rules and ontologies by interfacing of reasoners. In J. Alferes, J. Bailey, W. May, & U. Schwertel (Eds.), Proceedings of the 4th International Conference on Principles and Practice of Semantic Web Reasoning (LNCS 4187, p. 33-47).
4. The Description Logic Handbook
5. On the relative expressiveness of description logics and predicate logics
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献