Hybrid Measure of Agreement and Expertise for Ontology Matching in Lieu of a Reference Ontology. Issue 5 (19th October 2015)

• Record Type: Journal Article
• Authors: Al Boni, Mohammad; Anderson, Derek T.; King, Roger L.
• Is Part Of: International journal of intelligent systems. Volume 31:Issue 5(2016)
• Page Start: 502
• Page End: 525
• Publication Date: 2015-10-19
• Subjects: Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.21792
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract : Ontologies have been widely used as a knowledge representation framework, and numerous methods have been put forth to match ontologies. It is well known that ontology matchers behave differently in various domains, and it is a challenge to predict or characterize their behavior. Herein, a hybrid expertise‐agreement aggregation strategy is proposed. Although others rely on the existence of a reference ontology, this typically does not exist in the real world. In this article, the fuzzy integral (FI) is used to aggregate multiple ontology matchers in lieu of a reference ontology. Specifically, we present a measure of expertise and fuse it with our previous agreement measure that is motivated by crowd sourcing to improve recall. This way, any available domain knowledge, in terms of partial ordering of a subset of inputs, can be included in the decision‐making process. By adding the domain knowledge to the agreement model, we are able to reach the upmost performance. Preliminary results demonstrate the robustness of our approach across domains. Sensitivity analysis is also provided, which shows the limits to which extreme destructive expertise affects system performance.