Three-way conceptual knowledge updating in incomplete contexts

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2024-09-17 DOI:10.1016/j.ijar.2024.109299

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引用次数: 0

Abstract

We usually encounter incomplete data in daily life due to the uncertainty of data and limitation of data acquisition technology. In formal concept analysis, the incomplete formal context is used to reflect uncertain relation between objects and attributes caused by missing data. The conceptual knowledge of the incomplete formal context is presented by a kind of three-way concept called partially-known formal concept. As time passes and technology matures, some initially missing data becomes obtainable, the incomplete formal context is updated accordingly, and the corresponding concepts change as well. However, obtaining partially-known concepts from the updated context based on definition is time-consuming and does not fully utilize the conceptual knowledge implicit in the original context. In order to make full use of existing conceptual knowledge and acquire new concepts quickly and efficiently, we discuss how to obtain new partially-known formal concepts by updating original partially-known formal concepts, and design corresponding concept updating algorithms. Finally, through data experiments, we validate that our proposed concept update algorithm can significantly improve the efficiency of concept acquisition, especially when the updating rate is small.

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不完整语境中的三向概念知识更新

由于数据的不确定性和数据采集技术的局限性，我们在日常生活中经常会遇到数据不完整的情况。在形式概念分析中，不完整的形式语境用来反映由于数据缺失而导致的对象和属性之间的不确定关系。不完整形式语境的概念知识由一种称为部分已知形式概念的三向概念来呈现。随着时间的推移和技术的成熟，一些最初缺失的数据变得可以获取，不完整的形式语境也会随之更新，相应的概念也会发生变化。然而，根据定义从更新的上下文中获取部分已知概念不仅耗时，而且无法充分利用原始上下文中隐含的概念知识。为了充分利用已有的概念知识，快速高效地获取新概念，我们讨论了如何通过更新原有的部分已知形式概念来获取新的部分已知形式概念，并设计了相应的概念更新算法。最后，通过数据实验，我们验证了我们提出的概念更新算法能够显著提高概念获取的效率，尤其是在更新率较小的情况下。

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

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.