概念知识的分解提取与检索

Dmytro O. Terletskyi, S. Yershov
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引用次数: 1

摘要

对于许多现代基于知识的系统来说,提取隐藏和隐含知识、将其集成到知识库中,然后检索所需知识项的能力是知识处理的重要特征。然而,这些任务的复杂性取决于用于提取的知识来源的大小、用于集成提取的知识的知识库的大小以及用于检索所需知识项的搜索空间的大小。因此,在本文中,我们分析了对象同构类的内部语义依赖关系以及它们如何影响此类类的分解。由于同构类对象的所有子类构成一个完备的格,我们采用形式概念分析的方法对相应的概念格内的知识进行提取和检索。我们发现,这种方法没有考虑同构对象类中的内部语义依赖,因此,它可能导致在建模领域内语义不一致的形式概念的推理和检索。在面向对象的动态网络等知识表示模型中,将该算法应用于同构类对象的分解,进行动态知识提取和检索,并添加额外的过滤参数。因此,该算法通过在对象的同构类中只构造语义一致的子类来提取知识,然后根据属性和依赖查询对其进行过滤,检索知识。此外,我们引入了分解一致性系数,该系数可以估计算法可以减少多少知识提取的搜索空间并提高性能。为了演示改进算法的一些可能的应用场景,我们提供了一个通过分解特定同类对象类来提取和检索知识的适当示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decompositional Extraction and Retrieval of Conceptual Knowledge
An ability to extract hidden and implicit knowledge, their integration into a knowledge base, and then retrieval of required knowledge items are important features of knowledge processing for many modern knowledge-based systems. However, the complexity of these tasks depends on the size of knowledge sources, which were used for extraction, the size of a knowledge base, which is used for the integration of extracted knowledge, as well as the size of a search space, which is used for the retrieval of required knowledge items. Therefore, in this paper, we analyzed the internal semantic dependencies of homogeneous classes of objects and how they affect the decomposition of such classes. Since all subclasses of a homogeneous class of objects form a complete lattice, we applied the methods of formal concept analysis for the knowledge extraction and retrieval within the corresponding concept lattice. We found that such an approach does not consider internal semantic dependencies within a homogeneous class of objects, consequently, it can cause inference and retrieval of formal concepts, which are semantically inconsistent within a modeled domain. We adapted the algorithm for the decomposition of homogeneous classes of objects, within such knowledge representation model as object-oriented dynamic networks, to perform dynamic knowledge extraction and retrieval, adding additional filtration parameters. As the result, the algorithm extracts knowledge via constructing only semantically consistent subclasses of homogeneous classes of objects and then filters them according to the attribute and dependency queries, retrieving knowledge. In addition, we introduced the decomposition consistency coefficient, which allows estimation of how much the algorithm can reduce the search space for knowledge extraction and improves the performance. To demonstrate some possible application scenarios for the improved algorithm, we provided an appropriate example of knowledge extraction and retrieval via decomposition of a particular homogeneous class of objects.
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