Ontologies


Semantic Integration Through Invariants

AI Magazine

A semantics-preserving exchange of information between two software applications requires mappings between logically equivalent concepts in the ontology of each application. The challenge of semantic integration is therefore equivalent to the problem of generating such mappings, determining that they are correct, and providing a vehicle for executing the mappings, thus translating terms from one ontology into another. This article presents an approach toward this goal using techniques that exploit the model-theoretic structures underlying ontologies. With these as inputs, semiautomated and automated components may be used to create mappings between ontologies and perform translations. A major barrier to such interoperability is semantic heterogeneity: different applications, databases, and agents may ascribe disparate meanings to the same terms or use distinct terms to convey the same meaning.


BookReviews

AI Magazine

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.


BookReviews

AI Magazine

Building Large Knowledge-Based Systems (Addison-Wesley, Reading, Massachusetts, 1990, 372 pages, $39.75, ISBN O-201-51752-3) by Douglas B. Lenat and R. V. Guha is an interim report on the Microelectronic and Computer Technology Corporation (MCC) Cyc project. Cyc is an ambitious lo-year effort whose goal is to overcome the brittleness of contemporary expert systems by capturing the millions of facts and heuristics that MCC researchers consider to be the consensus reality that all intelligent beings share and that leads to common sense. As the authors state in their preface, "There are deep, important issues that must be addressed if we are ever to have a large intelligent knowledge-based program: What ontological categories would make up an adequate set for carving up the universe? What are the important things most human beings today know about solid objects? This book does an admirable job of presenting their research.


Book Review

AI Magazine

If you are interested in writing a review, contact chandra@cis. It is intended to be a "general textbook of knowledge-base analysis and design" (p. Its great strength is recognizing the need for an interdisciplinary approach, and the attempt at presenting the logical and philosophical foundations of knowledge representation under a unified view. Its great weakness is a lack of consistent rigor, which is needed in a textbook for newcomers to a subject. After some historical remarks and a first introductory chapter devoted to logic, Sowa immediately attacks the hard problems involved in choosing ontological categories, which lie at the heart of any knowledge representation project.


The Process Specification Language (PSL)

AI Magazine

However, interoperability among these manufacturing applications is hindered because the applications use different terminology and representations of the domain. These problems arise most acutely for systems that must manage the heterogeneity inherent in various domains and integrate models of different domains into coherent frameworks (figure 1). For example, such integration occurs in businessprocess reengineering, where enterprise models integrate processes, organizations, goals, and customers. Even when applications use the same terminology, they often associate different semantics with the terms. This clash over the meaning of the terms prevents the seamless exchange of information among the applications.


Ontology Research

AI Magazine

It is the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality. Ontology is, put simply, about existence. Like so many things, the term was borrowed by computer science and is rapidly becoming a buzzword in industry, tossed about by salesfolk, like all buzzwords, as if it were something everyone knew about. As it turns out, of course, very few people who use the word actually know what it means, and as a result, the actual meaning has changed, and is changing, over time. All computer scientists who claim allegiance to this field are constantly peppered with the same question, "What is an ontology?"


The CIDOC Conceptual Reference Module

AI Magazine

This ease has spurred an increasing interest from professionals, the general public, and consequently politicians to make publicly available the tremendous wealth of information kept in museums, archives, and libraries--the so-called memory organizations. Quite naturally, their development has focused on presentation, such as web sites and interfaces to their local databases. Now with more and more information becoming available, there is an increasing demand for targeted global search, comparative studies, data transfer, and data migration between heterogeneous sources of cultural contents. The reality of semantic interoperability is getting frustrating. In the cultural area alone, dozens of standard and hundreds of proprietary metadata and data structures exist as well as hundreds of terminology systems.


Ontology Translation for Interoperability Among Semantic Web Services

AI Magazine

Research on semantic web services promises greater interoperability among software agents and web services by enabling content-based automated service discovery and interaction and by utilizing. Although this is to be based on use of shared ontologies published on the semantic web, services produced and described by different developers may well use different, perhaps partly overlapping, sets of ontologies. Interoperability will depend on ontology mappings and architectures supporting the associated translation processes. The question we ask is, does the traditional approach of introducing mediator agents to translate messages between requestors and services work in such an open environment? This article reviews some of the processing assumptions that were made in the development of the semantic web service modeling ontology OWLS and argues that, as a practical matter, the translation function cannot always be isolated in mediators.


Semantics for Digital Engineering Archives Supporting Engineering Design Education

AI Magazine

This article introduces the challenge of digital preservation in the area of engineering design and manufacturing and presents a methodology to apply knowledge representation and semantic techniques to develop digital engineering archives. This work is part of an ongoing, multiuniversity effort to create cyber infrastructure-based engineering repositories for undergraduates (CIBER-U) to support engineering design education. The technical approach is to use knowledge representation techniques to create formal models of engineering data elements, work flows, and processes. With these techniques formal engineering knowledge and processes can be captured and preserved with some guarantee of long-term interpretability. The article presents examples of how the techniques can be used to encode specific engineering information packages and work flows.


Ontologies for Corporate Web Applications

AI Magazine

In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications. Commercial organizations are seeking to codify web services using such formalizations as the universal description, discovery, and integration (UDDI) specification. There are efforts to standardize intelligent agent technology, such as the Foundation for Intelligent Physical Agents (FIPA). These efforts at standardization must use ontologies if emerging internet applications are to be powered by semantics, the meaning behind advanced applications and their enterprise-level and community-level transactions. In this article, we discuss some issues that arise when ontologies are used to support corporate application domains such as electronic commerce (e-commerce) and some technical problems in deploying ontologies for realworld use. In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture and provide a short summary of ontology development tools. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications. The "vocabularies" for ontologies, as discussed in the introduction to this special issue, are distinct at different levels.