Abstract—

Both parts of this paper discuss the basic aspects of semantic computing, semantic technologies, and semantic applications applied to NL-text big data processing for knowledge extracting and decision making. The basic components of the corresponding systems and technologies are reviewed, which include ontologies and semantic models of their use, semantic resources, and semantic component. The semantic resources contain knowledge about the semantics and means for refinement of this semantics. The semantic component of the technology is used to formally describe the meaning of NL-entities and numerically evaluate their pairwise semantic similarity. The main focus of this part is on numerical models of pairwise semantic similarity of NL-entities. These models are important for solving tasks of text semantic clustering and classification and their various applications. Various types of semantic relatedness and semantic similarity measures for NL-entities in the context of semantic computing tasks are discussed and compared. Problems that constrain the practical use of semantic technologies for the development of semantic applications are analyzed.

中文翻译：

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语义应用的语义技术。第2部分。比较文本语义模型

摘要-

本文的两个部分都讨论了语义计算的基本方面，语义技术以及应用于NL文本大数据处理以进行知识提取和决策的语义应用。审查了相应系统和技术的基本组件，包括其使用的本体和语义模型，语义资源和语义组件。语义资源包含有关语义的知识以及完善此语义的方法。该技术的语义组件用于形式化描述NL实体的含义，并通过数值方式评估它们之间的成对语义相似性。这部分的主要重点是NL实体的成对语义相似性的数值模型。这些模型对于解决文本语义聚类和分类的任务及其各种应用非常重要。讨论和比较了在语义计算任务的上下文中用于NL实体的各种类型的语义相关性和语义相似性度量。分析了制约语义技术在语义应用程序开发中的实际使用的问题。