Ontology Merging and Reasoning Using Paraconsistent Logics

Ontology Merging and Reasoning Using Paraconsistent Logics

Cristian Cocos (Centre for Logic and Information, Saint Francis Xavier University, Antigonish, NS, Canada), Fahim Imam (Center for Research in Biological Systems, University of California - San Diego, La Jolla, CA, USA) and Wendy MacCaull (Centre for Logic and Information, Saint Francis Xavier University, Antigonish, NS, Canada)
Copyright: © 2012 |Pages: 17
DOI: 10.4018/ijkbo.2012100103

Abstract

Dealing with the inconsistencies that might arise during the ontology merging process constitutes a major challenge. The explosive nature of classical logic requires any logic-based merging effort to dissolve possible contradictions, and thus maintain consistency. In many cases, however, inconsistent information may be useful for intelligent reasoning activities. In healthcare systems, for example, inconsistent information may be required to provide a full clinical perspective, and thus any information loss is undesirable. The authors present a 4-valued logic-based merging system that exhibits inconsistency-tolerant behavior to avoid information loss.
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Introduction

Ontological knowledge bases are a knowledge representation (KR) style that has recently gained currency in the KR world, mostly spurred by Semantic Web (http://www.w3.org/) endeavors. The most prominent Semantic Web KR framework is the Web Ontology Language (OWL). For the present purposes, an ontology can be viewed as a formal theory formulated using the OWL language.

Ontologies have become widely effective for a range of applications, including software design, expert systems, database architectures, etc. The significance of ontologies has become well established in the healthcare and bio-informatics community, and the number of domain-specific, bio-medical, and healthcare ontologies is growing fast. An increasing number of them conform to various terminological standards. It has become a critical issue to share and reuse the combined but overlapping domain knowledge from the existing ontologies, especially those that conform to international terminological standards. One way to deal with this issue is known as ‘ontology merging.’ This kind of integration is in high demand where the goal is to generate a single coherent ontology that ensures the maximum reuse of knowledge from multiple source ontologies.

The significance of ontology merging or integration can be observed in the context of the Pan-Canadian Electronic Health Record (EHR) system. EHRs enhance the flow of information across multiple healthcare disciplines through the use of clinical terminologies and uniform language. Because of the diversity of various clinical terminologies, the challenge is to find a single terminology to represent all of the care-providing disciplines. Canada, for instance, has adopted SNOMED-CT® as the recommended clinical terminology for the EHR. SNOMED-CT is a terminology maintained by the International Health Terminology Standards Development Organization (IHTSDO). The Canadian Nurses Association (CNA), on the other hand, recommends the International Classification for Nursing Practice (ICNP®) to represent nursing practice, as SNOMED-CT is more focused on the bio-medical perspective of healthcare. The CNA recommends that ICNP and SNOMED-CT collaborate to ensure that SNOMED-CT is developed in such a way so as to effectively represent nursing practice in EHR. This collaboration has recently materialized, among others, via the ICNP-SNOMED-CT mapping efforts currently underway under IHTSDO patronage. Merging or integrating ICNP and SNOMED-CT terminologies is invaluable for the overall success of EHRs to provide extensive representational capacity (Imam, MacCaull, & Kennedy, 2007; William, 2006).

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