CONCEPT SIMILARITY IN FORMAL CONCEPT ANALYSIS WITH MANY-VALUED CONTEXTS

Formal Concept Analysis (FCA) is a mathematical framework which can also support critical activities for the development of the Semantic Web. One of them is represented by Similarity Reasoning, i.e., the identification of different concepts that are semantically close, that allows users to retrieve information on the Web more efficiently. In order to model uncertainty information, in this paper FCA with many-valued contexts is addressed, where attribute values are intervals, which is referred to as FCA with Interordinal scaling (IFCA). In particular, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated, although the increasing interest in the literature in this topic.

Automated summary

Formal Concept Analysis (FCA) is a mathematical framework that can support critical activities for the development of the Semantic Web, such as Similarity Reasoning, which aims to identify semantically close concepts. This paper addresses FCA with Interordinal scaling (IFCA) to model uncertainty information, proposing a method for evaluating concept similarity in IFCA, a topic of increasing interest in the literature.