This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

/pdf/knowledge-engineering-principles-and-methods-6do1hbj75r.pdf

Knowledge engineering: principles and methods

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

Explanation in artificial intelligence: Insights from the social sciences

https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionPart1.pdf

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

This survey provides a conceptual introduction to ontologies and their role in information systems and AI. The authors also discuss how ontologies clarify the domain's structure of knowledge and enable knowledge sharing.

/pdf/what-are-ontologies-and-why-do-we-need-them-njqdwezxhe.pdf

What are ontologies, and why do we need them?

The computational complexity of abduction

What Are They? Why Do We Need Them?

We explore the meanings of the terms ‘structure’, ‘behaviour’, and, especially, ‘function’ in engineering practice. Computers provide great assistance in calculation tasks in engineering practice, but they also have great potential for helping with reasoning tasks. However, realising this vision requires precision in representing engineering knowledge, in which the terms mentioned above play a central role. We start with a simple ontology for representing objects and causal interactions between objects. Using this ontology, we investigate a range of meanings for the terms of interest. Specifically, we distinguish between function as effect on the environment, and a device-centred view of device function. In the former view, function is seen as an intended or desired role that an artifact plays in its environment. We identify an important concept called mode of deployment that is often left implicit, but whose explicit representation is necessary for correct and complete reasoning. We discuss the task of design and design verification in this framework. We end with a discussion that relates needs in the world to functions of artifacts created to satisfy the needs.

/pdf/function-in-device-representation-4n41yfgyfg.pdf

Function in Device Representation

Much of the work on ontologies in AI has focused on describing some aspect of reality: objects, relations, states of affairs, events, and processes in the world. A goal is to make knowledge sharable, by encoding domain knowledge using a standard vocabulary based on the ontology. A parallel attempt at identifying the ontology of problem-solving knowledge would make it possible .to share problem-solving methods. For example, when one is dealing with a type of problem known as abductive inference, the following are some of the terms that recur in the representation of problem-solving methods: hypotheses, explanatory coverage, evidence, degree of confidence, plausibility, composite hypothesis, etc. Method ontology, in good part, is task- and method-specific. "Generic Tasks," "Heuristic Classification," "Task-specific Architectures," and "Task Structures" are representative bodies of work in the knowledgesystems area that have focused on problem-solving methods. However, connections have not been made to work that is explicitly concerned with ontologies. Making such connections is the goal of this paper.

/pdf/the-ontology-of-tasks-and-methods-1yg48bfmgl.pdf

John R. Josephson

Papers

What are ontologies, and why do we need them?

The computational complexity of abduction

What Are They? Why Do We Need Them?

Function in Device Representation

The Ontology of Tasks and Methods