A comparison of upper ontologies

We propose an approach to achieve appropriate exchange of services and data in distributed systems subject to semantic heterogeneity. We assume differentiated ontologies: that terms have formal definitions as concepts related to other concepts, that local concepts inherit from concepts that are shared, and that most or all primitives are shared. We then develop measures of description compatibility using the structure of the source and target definitions. We evaluate these measures by generating description-logic ontologies in artificial worlds. In our simulations, the "meaning" of a concept is its denotation in a finite universe of instances. The accuracy of the description-compatibility measures can thus be judged by their success in predicting the overlap of concept denotations. Description compatibility can be used to guide agent search for services across communities that subscribe to differentiated ontologies. 2 Differentiated Ontologies To enable calculation of the compatibility measures, we make certain assumptions about the syntactic representation of terms to be mapped. Structures that satisfy these assumptions are differentiated ontologies. Assumption 1: Terms are defined as concepts in formal ontologies. Ontologies are sets of concept definitions. Formal ontologies use logic to define concepts by their relations to other concepts. For example, chair might be defined as a-kind-of furniture that a person can sit-upon. 1 The concept chair denotes the set of objects that are chairs; the instance chair001 denotes a particular chair object. The logic must, like description logic, support computational inference of subsumption (Woods 1991). Concept A subsumes B if and only if every instance of B is always an instance of A. Highchair, for example, might be defined as a-kind-of furniture that a toddler can sit-upon; by comparing the definitions for chair and highchair and person and toddler, respectively, description logic can deduce that highchair is a-kind-of chair. (See Weinstein and Birmingham (Weinstein and Birmingham 1998) for an expanded description of "formal ontology"; in this paper, Section 4 specifies the representation that we use in our simulations.) The structure of ontological definition provides a basis for syntactic comparison. The precision required by logic, and in particular the support for subsumption inference, is required to enable calculation of the compatibility measures. Whereas Assumption 1 is fundamental to our enterprise, the following assumptions are "soft" in that they can be violated, but increasing frequency of violation yields increasingly poor results for our description-compatibility measures. Assumption 2: Concepts inherit definitional structure from concepts that are shared. This assumption identifies the form of commonality that we use to enable automatic mapping. Note that mapping between concepts in differentiated ontologies is equivalent to mapping between concepts in a single ontology that provides separate spaces for concept names. Assumption 3: Primitive concepts and relations are shared. Non-primitive definitions have both necessary and sufficient conditions, but primitives have only necessary conditions. For example, given the primitive (C ⇒ D), we cannot infer that instance j ∈ C unless this is asserted explicitly. Clearly, there can be no basis for matching a primitive element of a concept definition to any element of a concept in an ontology where the primitive is undefined. We expect differentiated ontologies to occur in two important contexts. First, architects may design infrastructures for multi-agent systems that include one or more generic ontologies to which all agents are expected to subscribe. As systems change over time, developers will create local versions of ontologies that are specialized to help meet the needs of particular user communities. Over time, large ontological structures will develop, and communities of agents will be defined by the ontologies they use to communicate. Agents will interact with agents in their own community, but may use the techniques

Expert Systems, 2008

Domain ontologies and knowledge-based systems have become very important in the agent and semantic web communities. As their use has increased, providing means of resolving semantic differences has also become very important. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies. We also discuss some key issues that still need to be addressed if we were to move from semi to fully automated approaches to provide consensus among heterogeneous ontologies. .

2005

Abstract Metadata scheme harmonization and the development of'generic'metadata standards have been proposed and studied as techniques to alleviate the problem of metadata integration. However, the use of existing upper onotologies as base models for metadata schemas provides an alternative that allows for a significant reuse of knowledge representations that have been evaluated and refined in the last years. In that direction, this paper explores the linking of Dublin Core terms to the OpenCyc knowledge base.

The idea of the semantic web is to add machine process able information to web-based data in order to realize interoperability. Ontology is a shared conceptualization of knowledge representation of particular domain. These are used for the enhancement of semantic information explicitly. Ontologies play a prominent role in the concept of the semantic web to provide semantic information for assisting communication among heterogeneous information repositories. Ontology Interoperability provides the reusability of ontologies Different domain experts and ontology engineers create different ontologies for the same or similar domain depending on their data modeling requirements. These cause ontology heterogeneity and inconsistency problems. As increasing numbers of ontologies are developed by diverse communities, the demand for rapid ontology mapping is arising. For more better and precise results ontology mapping is the solution. As their use has increased, providing means of resolving semantic differences has also become very important. Papers on ontology interoperability report the results on different frameworks and this makes their comparison almost impossible. Therefore, the main focus of this paper will be on providing some basics of ontology interoperability and briefly introducing its different approaches. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies and its related techniques and tools. https://sites.google.com/site/ijcsis/

In realistic applications, it is often desirable to integrate different ontologies 1 into a single, reconciled ontology. Ideally, one would expect the individual ontologies to be developed as independently as possible from the rest, and the final reconciliation to be seamless and free from unexpected results. This would allow for the modular design of large ontologies and would facilitate knowledge reuse tasks. Few ontology development tools, however, provide any support for integration, and there has been relatively little study of the problem at a fundamental level.

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance and accuracy on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.

informatik.uni-bremen.de

This paper presents two contributions to the field of heterogeneous ontology engineering: (1) we suggest a way to specify ontologies in a heterogeneous, human-readable, and reasoner-friendly way. In particular, our approach enables the designer to split up a heterogeneous ontology into semantically meaningful parts, and employ dedicated reasoning tools to them; (2) as an application, we discuss the problem of refinement of heterogeneous ontologies and give a logically precise notion of heterogeneous sub-ontology.

This paper deals with the problem of handling semantic heterogeneity during schema integration. Semantics refer to the meaning of data in contrast to syntax, which solely defines the structure of schema elements. We focus on the part of semantics related to the meanings of terms used to name schema elements. Our approach does not rely on the names of the schema elements or the structure of the schema. Instead, we present an approach based on formal ontologies presented in a logical language for integrating schemas to produce global schemas. Semantic similarity relations between definitions in formal ontologies are defined and used for merging formal ontologies. We show how similarity relations are discovered by a reasoning system using a higher level ontology. The result of the merging process is used for schema integration. The result of schema integration can be used as a global schema in a tightly coupled federated database. Afterwards, we illustrate how the produced global schema can help for the mapping of data elements.

IEEE Expert / IEEE Intelligent Systems, 2008

1999

As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user's actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms.