Automatic structuring of knowledge bases by conceptual clustering

Abstract

An important structuring mechanism for knowledge bases is building an inheritance hierarchy of classes based on the content of their knowledge objects. This hierarchy facilitates group-related processing tasks such as answering set queries, discriminating between objects, finding similarities among objects, etc. Building this hierarchy is a difficult task for the knowledge engineer. Conceptual clustering may be used to automate or assist the engineer in the creation of such a classification structure. This article introduces a new conceptual clustering method which addresses the problem of clustering large amounts of structured objects. The conditions under which the method is applicable are discussed.

Computing Concept Lattices with Clustering Approaches

“…Introduced in the early 1980s as a formalization of the concept of `concept' [10], FCA has over the years grown to a powerful theory for data analysis, information retrieval, and knowledge discovery [11]. In artificial intelligence (AI), FCA is used as a knowledge representation mechanism [12] and as conceptual clustering method [9] for crisp concepts. In database theory, FCA has been extensively used for class hierarchy design and management [13]- [15].…”

“…Its usefulness for the analysis of data stored in relational databases has been demonstrated with the commercially used management system TOSCANA for Conceptual Information Systems [17]. FCA is a branch of lattice theory motivated by the need for a clear mathematization of the notions of concept and conceptual hierarchy [6]- [8], and knowledge acquisition [9].…”

Generalization lattices

“…To provide a high-level view of the generalization operations permitted on data, various researchers have proposed similar structures, here called generalization lattices ( GLs), but variously called type hierarchies [12,9], concept generalization graphs [10], domain generalization graphs [6], dependency lattices [7], and attribute lattices [3]. A generalization lattice is an effective form of knowledge representation for encoding prior domain knowledge relevant to generalization.…”

Discovering shared conceptualizations in folksonomies

“…FCA has grown over the years to a powerful theory for data analysis, information retrieval, and knowledge discovery [65]. In Artificial Intelligence (AI), FCA is used as a knowledge representation mechanism [66] and as conceptual clustering method [63,15,48]. In database theory, FCA has been extensively used for class hierarchy design and management [49,77,20,72,56,27].…”