Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures

E. Manziuk, O. Barmak, Iu. V. Krak, O. Pasichnyk, P.M. Radiuk, O. Mazurets
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Abstract

The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol- ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.
本体的语义对齐,有意义的范畴与描述性结构的泛化
提出的工作解决了本体组件与广义结构化语料库的语义对齐问题。研究领域是指确定人工智能中信任特征的领域。在通用对齐系统的语义组件层面提出了一种对齐方法。该方法是更广泛的校准系统的一个组成部分,并在有意义的对应级别上比较实体。此外,在建议的技术中只考虑对齐实体的描述性内容。描述性内容可以用各种命名的id和语义关系来表示。该方法定义了一个基本的控制论和一个特定的对齐结构。信息范围形式的语义对应是由对齐结构形成的。这样,在对齐结构的一侧形成了一个实体,就有意义的描述性而言,它将以最好的有意义的方式与本体中的实体相对应。有意义的描述是信息范围的填充。信息范围是泛化的最终形式,可以由实体、一组实体和它们的部分并集组成。反过来,实体是位于层次结构较低级别的属性的概括,而实体又是描述符的组合。描述符是表示主要内容的概括的基本元素。描述符可以定义知识库中的原子内容,并且只表示内容的一个特定方面。因此,有意义的元素不是自给自足的,可以以属性的形式表现为单独的有意义,作为对齐的有意义的最小表示。描述符还可以在信息框架、实体和属性级别上补充内容。信息范围形式的对齐本质不能用描述符或它们的组合来表示。发生这种情况是因为描述性描述符没有以完整的通信单元形式表示内容。信息范围表示的最小结构是以属性的形式。这种建立对齐语义层次对应关系的组织形式允许您对具有复杂语义映射形式的区域的信息内容进行结构化和形式化。泛化的层次表示不仅可以简化语义对齐的形式化,而且可以在描述符级别上形成具有内容离散化可能性的信息实体。反过来,描述符可以在泛化层次结构的任意级别上扩展意义。这提供了量化的信息内容和灵活性的校准系统与离散在描述符的水平。该方法用于形式化本体实体的语义对齐和信息的结构化表示区域。
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