CONCEPT SIMILARITY IN FORMAL CONCEPT ANALYSIS WITH MANY-VALUED CONTEXTS

Formica Anna

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CONCEPT SIMILARITY IN FORMAL CONCEPT ANALYSIS WITH MANY-VALUED CONTEXTS

机译：CONCEPT SIMILARITY IN FORMAL CONCEPT ANALYSIS WITH MANY-VALUED CONTEXTS

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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.

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《Computing and informatics》 |2021年第3期|469-488|共20页
作者
Formica Anna;
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CNR, Ist Anal Sistemi & Informat IASI, Via Taurini 19, I-00185 Rome, Italy;

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正文语种英语
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Formal concept analysis; similarity reasoning; many-valued contexts; FCA with interordinal scaling;

CONCEPT SIMILARITY IN FORMAL CONCEPT ANALYSIS WITH MANY-VALUED CONTEXTS

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