The State of the Art in Ontology Design: A Survey and Comparative Review

By Noy, Natalya Fridman; Hafner, Carole D. | AI Magazine, Fall 1997 | Go to article overview

The State of the Art in Ontology Design: A Survey and Comparative Review


Noy, Natalya Fridman, Hafner, Carole D., AI Magazine


The major goal of this article is to develop a framework for comparing various projects in ontology design and put a number of prominent ontologies in this framework. According to Webster's dictionary (Woolf 1981), ontology is a particular theory about the nature of being or the kinds of existent. The task of intelligent systems in computer science is to formally represent these existents. A body of formally represented knowledge is based on conceptualization. Conceptualization consists of a set of objects, concepts, and other entities about which knowledge is being expressed and of relationships that hold among them. Every knowledge model is committed to some conceptualization, implicitly or explicitly. An explicit specification of this conceptualization is called an ontology (Gruber 1993). Formally, an ontology consists of terms, their definitions, and axioms relating them (Gruber 1993); terms are normally organized in a taxonomy.

Most of the researchers in the area of ontology design agree that the current important goals of ontology research are to (1) make ontologies sharable by developing common formalisms and tools; (2) develop the content of ontologies (ontology design); and (3) compare, gather, translate, and compose different ontologies. Recent work in ontology design has produced a range of different projects, from ontologies that represent general world knowledge to domain-specific ontologies to knowledge representation systems that embody ontological frameworks. There is an agreement in the ontology engineering community that it would be beneficial to be able to integrate ontologies so that they can share and reuse each other's knowledge. If one ontology, for example, has a well-developed theory of time, another ontology (say, the one representing biology experiments) could then use this theory without having to reinvent it. There is also an understanding that achieving the interoperability of ontologies is a challenging task. For smooth integration to be (at least partially) possible, the first thing to do is to look at the ontology projects that already exist and are fairly well developed and consider the differences and similarities in the way they treat some basic knowledge representation aspects. With this understanding, we can see where there is some common base and what the obstacles are to the integration of different ontologies.

Identifying a framework for comparing ontologies and placing a number of the more prominent existing projects in this framework is the objective of the study presented here. After giving a brief introduction to a number of ontology projects, we compare them with respect to what they were created for; what the design process was; and how they treat certain fundamental issues in representing knowledge, such as taxonomies, properties, and relations. We identify common themes and consider different approaches to these issues. We try to single out some major approaches in each dimension, group the projects according to this categorization, and point out the ones that do not fit in this categorization.

Table 1. List of Projects Used in This Study.

Project Name                                Project Description

CYC                                         A general ontology for
                                            commonsense knowledge
                                            to facilitate reasoning

K. Dahlgren's ontology                      A linguistically
                                            motivated ontology of
                                            commonsense knowledge

GENERALIZED UPPER MODEL                     A general task and
                                            domain independent
                                            ontology that is
                                            designed to support
                                            sophisticated natural
                                            language processing in
                                            different languages

GENSIM                                      A genetic simulation
                                            system that represents
                                            and models
                                            enzymatically catalyzed
                                            biomedical reactions

Knowledge interchange format (KIF)          A language for defining
                                            ontologies that has
                                            declarative semantics
                                            and is based on
                                            first-order predicate
                                            calculus

PLINIUS Project                             An ontology for
                                            representing mechanical
                                            properties of ceramic
                                            materials

J. 

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