Knowledge Engineering Practices Research Articles

Knowledge machines

AbstractThe World Wide Web has had a notable impact on a variety of epistemically relevant activities, many of which lie at the heart of the discipline of knowledge engineering. Systems like Wikipedia, for example, have altered our views regarding the acquisition of knowledge, while citizen science systems such as Galaxy Zoo have arguably transformed our approach to knowledge discovery. Other Web-based systems have highlighted the ways in which the human social environment can be used to support the development of intelligent systems, either by contributing to the provision of epistemic resources or by helping to shape the profile of machine learning. In the present paper, such systems are referred to asknowledge machines. In addition to providing an overview of the knowledge machine concept, the present paper reviews a number of issues that are associated with the scientific and philosophical study of knowledge machines. These include the potential impact of knowledge machines for the theory and practice of knowledge engineering, the role of social participation in the realization of knowledge-based processes, and the role of standardized, semantically enriched data formats in supporting thead hocassembly of special-purpose knowledge systems and knowledge processing pipelines.

Understanding, building and using ontologies

I defend here the thesis of the independence between domain knowledge and problem-solving knowledge, arguing against the dominance of the so-called ‘‘interaction problem’’ mentioned in a recent paper by Van Heijst, Schreiber and Wielinga to dispute the feasibility of a single domain ontology shared by a number of different applications. The main point is that reusability across multiple tasks or methods can and should be systematically pursued even when modelling knowledge related to a single task or method. Under this view, I discuss how the principles of formal ontology and ontological engineering can be used in the practice of knowledge engineering, focusing in particular on the interplay between general ontologies, method ontologies and application ontologies, and on the role of ontologies in the knowledge engineering process. I will then stress the role of domain analysis, often absent in current methodologies for the development of knowledge-based systems.

Concepts, attributes and arbitrary relations: Some linguistic and ontological criteria for structuring knowledge bases

There is a subtle risk of ambiguity in the choice between concepts and roles forced by current KL-ONE-like languages, since many roles may be concepts as well. In this paper we explore the ontological foundations of the role/concept relationship, and analyze its implications on the practice of knowledge engineering. We criticize the current interpretation of KL-ONE roles as arbitrary relations, which vanishes their original meaning and makes them identical to slots. We suggest to call attributes those concepts which actually act as conceptual components, and propose a formal semantics which binds these concepts to their corresponding relations.

Towards the Principled Engineering of Knowledge

The acquisition of expert knowledge is fundamental to the certain of expert systems. The conventional approach to building expert systems assumes that the knowledge exists, and that it is feasible to find an expert who has the knowledge and can articulate it in collaboration with a knowledge engineer. This article considers the practice of knowledge engineering when these assumptions can not be strictly justified. It draws on our experiences in the design of VLSI design methods, and in the prototyping of an expert assistant for VLSI design. We suggest methods for expanding the practice of knowledge engineering when applied to fields that are fragmented and undergoing rapid evolution. We outline how the expanded practice can shape and accelerate the process of knowledge generation and refinement. Our examples also clarify some of the unarticulated present practice of knowledge engineering.