A further understanding for four types of concepts in incomplete contexts based on their connections

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Intelligence Pub Date : 2026-09-30 DOI:10.1007/s10489-026-07311-0

Xue Tian, Ruisi Ren, Ling Wei, Yanhong She

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引用次数: 0

Abstract

In formal concept analysis, incomplete data are represented as incomplete formal contexts. To convey the meanings of “jointly certainly possessing (possessed)” and “jointly possibly possessing (possessed)”, partially-known formal concepts are proposed. Partially-known formal concepts have three specific forms: SE-ISI concept, ISE-SI concept and ISE-ISI concept, reflecting uncertain information of an incomplete context in different aspects. Since partially-known formal concepts and formal concepts in the least and greatest completions are all defined by the upper and lower derivation operators, there must be some connections among them. Exploring these connections can further help us acquire knowledge of raw data. In this paper, we first study the connections between partially-known formal concepts and formal concepts in the least and greatest completions, and then present methods to construct partially-known concept lattices based on formal concept lattices. Secondly, we examine the connections among the three kinds of partially-known concepts from the viewpoints of single concept and the set of extents (intents), demonstrating methods for mutual conversion among them. Finally, based on these connections, algorithms are designed to acquire partially-known formal concepts from formal concepts, and experiments are conducted to validate the feasibility as well as the stability of the algorithms under different data missing mechanisms. When the incomplete contexts contain relatively few concepts, the algorithm exhibits superior performance.

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在不完全语境中，根据概念之间的联系，进一步理解四种类型的概念

在形式概念分析中，不完整的数据被表示为不完整的形式语境。为了表达“共同肯定占有”和“共同可能占有”的含义，提出了部分已知的形式概念。部分已知的形式概念有SE-ISI概念、ISE-SI概念和ISE-ISI概念三种具体形式，从不同方面反映了不完整语境的不确定信息。由于部分已知的形式概念和最小和最大补全中的形式概念都是由上下导数算子定义的，因此它们之间必然存在某种联系。探索这些联系可以进一步帮助我们获得原始数据的知识。本文首先研究了部分已知形式概念与形式概念在最小和最大补全中的联系，然后提出了基于形式概念格构造部分已知概念格的方法。其次，我们从单一概念和范围（意图）的角度考察了三种部分已知概念之间的联系，并给出了它们之间相互转换的方法。最后，基于这些联系，设计了从形式概念中获取部分已知形式概念的算法，并通过实验验证了算法在不同数据缺失机制下的可行性和稳定性。当不完整上下文包含相对较少的概念时，算法表现出较好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Intelligence 工程技术-计算机：人工智能

CiteScore

6.60

自引率

20.80%

发文量

1361

审稿时长

5.9 months

期刊介绍： With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.