Tacit knowledge mining algorithm based on linguistic truth-valued concept lattice

TL;DR

This paper develops a 6-ary linguistic truth-valued concept lattice model and algorithms to mine tacit knowledge by analyzing attribute-based contexts and their structural consistency.

Contribution

It introduces a novel 6-ary linguistic truth-valued concept lattice model and algorithms for tacit knowledge mining based on linguistic truth values.

Findings

Established a 6-ary linguistic truth-valued concept lattice model.

Proposed algorithms for generating congener contexts and constructing the lattice.

Provided necessary and sufficient conditions for tacit knowledge formation.

Abstract

This paper is the continuation of our research work about linguistic truth-valued concept lattice. In order to provide a mathematical tool for mining tacit knowledge, we establish a concrete model of 6-ary linguistic truth-valued concept lattice and introduce a mining algorithm through the structure consistency. Specifically, we utilize the attributes to depict knowledge, propose the 6-ary linguistic truth-valued attribute extended context and congener context to characterize tacit knowledge, and research the necessary and sufficient conditions of forming tacit knowledge. We respectively give the algorithms of generating the linguistic truth-valued congener context and constructing the linguistic truth-valued concept lattice.