AI

Original abstract: Information sharing and decision coordination are central problems for large- scale product development. However, existing computer tools mainly support isolated tasks, such as geometric modeling and manufacturing process planning. This paper proposes a knowledge representation to support knowledge sharing and communication for cooperative product development. The representation is being designed as a knowledge medium for human organizations, rather than a language for data exchange between tools. Existing product data, models, documents and other forms of shared knowledge are encapsulated into a shared knowledge base a design elements. Relationships among design elements, and annotations describing their contents, are represented explicitly. The representation will afford variable levels of formalization of design knowledge. The minimal, “semiformal” level is an encapsulation of design elements as opaque objects and untyped relations among them (“hyperlinks”). Formal annotations on design elements and relationships can be incrementally enriched. The highest degree of formality includes declarative theories that support automated reasoning about how design team need to be notified. The paper analyzes the relationships can be incrementally enriched. The highest degree of formality includes declarative theories that support automated reasoning about how design changes impact other parts of the design and which members of the design team need to be notified. The paper analyzes the relationship between levels of formality in the shared representation and the computational services they enable.

One of the first journal articles presenting domain ontologies as a research contribution.

In the VT/Sisyphus experiment, a set of problem solving systems were being built against a common specification of a problem. An important hypothesis was that the specification could be given, in large part, as a common ontology. This article is that ontology. This ontology is different than normal software specification documents in two fundamental ways. First, it is formal and machine readable (i.e. in the KIF/Ontolingua syntax). Second, the descriptions of the input and output of the task to be performed include domain knowledge (i.e. about elevator configuration) that characterize semantic constraints on possible solutions, rather than describing the form (data structure) of the answer. The article includes an overview of the conceptualization, excerpts from the machine-readable Ontolingua source files, and pointers to the complete ontology library available on the Internet.

Possibly the first refereed publication of an AI ontology, explicitly called out as an ontology.  Defines a formal axiomatization of the mathematics sufficient to represent modern engineering models. The HTML version of this paper is deeply cross indexed and contains the entire ontology in machine and human readable form.

Original abstract: We describe an ontology for mathematical modeling in engineering. The ontology includes conceptual foundations for scalar, vector, and tensor quantities, physical dimensions, units of measure, functions of quantities, and dimensionless quantities. The conceptualization builds on abstract algebra and measurement theory, but is designed explicitly for knowledge sharing purposes. The ontology is being used as a communication language among cooperating engineering agents, and as a foundation for other engineering ontologies. In this paper we describe the conceptualization of the ontology, and show selected axioms from definitions. We describe the design of the ontology and justify the important representation choices. We offer evaluation criteria for such ontologies and demonstrate design techniques for achieving them.

This publication won the Best Paper award at a prestigious engineering conference. The design of products by multi-disciplinary groups is a knowledge intensive activity. Collaborators must be able to exchange information and share some common understanding of the information’s content. The hope, however, that a centralized standards effort will lead to integrated tools spanning the needs of engineering collaborators is misplaced. Standards cannot satisfy the information sharing needs of collaborators, because these needs cannot be standardized.

This paper discusses the design and use of a shared representation of knowledge (language and vocabulary) to facilitate communication among specialists and their tools. The paper advances the opinion that collaborators need the ability to establish and customize knowledge sharing agreements (i.e. mutually agreed upon terminology and definitions) that are usable by people and their machines. The paper describes a formal approach to representing engineering knowledge, describes its role in a computational framework that integrates a heterogeneous mix of software tools, and discusses its relationship to current and emerging data exchange standards.

Interview with a Semantic Web organization, published in their journal of record, in which I argue that ontologies are designed in a social context.  It was a radical idea back then that the meaning of formal knowledge representations are situated in human society. It also predicted that standardizing ontologies top down would fail.

The manifesto publication of the DARPA Knowledge Sharing Effort, describing the layers which eventually morphed and survived somewhat scathed in the formalisms of the Semantic Web.  Historically interesting given what has happened since with XML.

Provides a definition of ontology as a technical term for computer science, tracing its historical context from philosophy and AI.  Definitional article in the encyclopedia of database systems on ontology. Update of 1993 ontology definition

For a recent citation, see: Gruber T. (2016) Ontology. In: Liu L., Özsu M. (eds) Encyclopedia of Database Systems. Springer, New York, NY.

One of the first attempts at a software development methodology for ontologies. Introduces the notion that ontologies are design and should be amenable to engineering methodologies. Proposes five design criteria for ontologies.

Original abstract: Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.

Proposes a class of applications called Collective Knowledge Systems, which are the “killer apps” for the integration of the Social Web (2.0) and the Semantic Web. Characteristics of these systems, principles, and examples from real applications are included.

Original abstract: What can happen if we combine the best ideas from the Social Web and Semantic Web?  The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions.  The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources.  What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information?  This paper proposes a class of applications called collective knowledge systems, which unlock the “collective intelligence” of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.