Formal Ontology, Conceptual Analysis and Knowledge Representation

DMCA

Formal Ontology, Conceptual Analysis and Knowledge Representation (1995)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Nicola Guarino

Venue:	INTERNATIONAL JOURNAL OF HUMAN AND COMPUTER STUDIES
Citations:	227 - 13 self

BibTeX

@ARTICLE{Guarino95formalontology,,
    author = {Nicola Guarino},
    title = {Formal Ontology, Conceptual Analysis and Knowledge Representation},
    journal = {INTERNATIONAL JOURNAL OF HUMAN AND COMPUTER STUDIES},
    year = {1995},
    volume = {43},
    pages = {625--640}
}

OpenURL

Abstract

The purpose of this paper is to defend the systematic introduction of formal ontological principles in the current practice of knowledge engineering, to explore the various relationships between ontology and knowledge representation, and to present the recent trends in this promising research area. According to the "modelling view" of knowledge acquisition proposed by Clancey, the modeling activity must establish a correspondence between a knowledge base and two separate subsystems: the agent's behavior (i.e. the problem-solving expertize) and its own environment (the problem domain). Current knowledge modelling methodologies tend to focus on the former subsystem only, viewing domain knowledge as strongly dependent on the particular task at hand: in fact, AI researchers seem to have been much more interested in the nature of reasoning rather than in the nature of the real world. Recently, however, the potential value of task-independent knowlege bases (or "ontologies") suitable to large scale integration has been underlined in many ways. In this paper, we compare the dichotomy between reasoning and representation to the philosophical distinction between epistemology and ontology. We introduce the notion of the ontological level, intermediate between the epistemological and the conceptual level discussed by Brachman, as a way to characterize a knowledge representation formalism taking into account the intended meaning of its primitives. We then discuss some formal ontological distinctions which may play an important role for such purpose.

Keyphrases

knowledge representation conceptual analysis formal ontology modelling view important role current practice modeling activity former subsystem ai researcher intended meaning knowledge base current knowledge modelling methodology conceptual level particular task potential value recent trend knowledge acquisition problem domain real world knowledge engineering formal ontological distinction knowledge representation formalism many way various relationship problem-solving expertize task-independent knowlege base philosophical distinction ontological level domain knowledge formal ontological principle systematic introduction large scale integration separate subsystem promising research area

DMCA