Ontologies/Knowledge Sharing

A rebuttal to a popular anti-ontology blog, with a constructive call to action.  Showed how “Folksonomy” isn’t incompatible with formal ontology and showed how to get the benefits of both.

Originally presented as an invited keynote to the First on-Line conference on Metadata and Semantics Research (MTSR’05) and released on www.metadata-semantics.org (no longer active).  Published to the journal in 2007.

Reference: Cutkosky, M. R.; Engelmore, R. S.; Fikes, R. E.; Genesereth, M. R.; Gruber, T. R.; Mark, W. S.; Tenenbaum, J. M.; & Weber, J. C. PACT: An Experiment in Integrating Concurrent Engineering Systems. 1993.

Abstract: The Palo Alto Collaborative Testbed (PACT) is a joint experiment in concurrent engineering being pursued by research groups at Stanford University, Lockheed, Hewlett-Packard, and Enterprise Integration Technologies. The current prototype integrates four preexisting concurrent engineering systems into a common framework. Each of the individual systems is used to model different aspects of a small robotic manipulator, and to reason about them from a different discipline (dynamics, digitial electronics, and software). The initial PACT experiments have explored knowledge sharing in the context of a distributed simulation and simple incremental redesign scenario.

Notes: Submitted February 1993.

Abstract: Effective information sharing and decision coordination are vital to collaborative product development and integrated manufacturing. However, typical special-purpose CAE systems tend to isolate information at tool boundaries, and typical integrated CAE systems tend to limit flexibility and process innovation. The SHADE (SHAred Dependency Engineering) project strikes a balance between these undesirable extremes by supporting reconfigurable exchange of engineering knowledge among special-purpose CAE systems. SHADE’s approach has three main components: a shared knowledge representation (language and domain-specific vocabulary), protocols supporting information exchange for change notification and subscription, and facilitation services for content-directed routing and intelligent matching of information consumers and producers.

An ontology-based knowledge sharing API for AI people.

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.