使用描述逻辑工具定义语义相似度

O. Zakharova
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引用次数: 1

摘要

建立信息的语义相似度是解决任何信息检索任务过程中不可或缺的一部分,包括与大数据处理、语义web服务发现、信息分类和分类等相关的任务。用于确定信息语义相似度的定量指标的特殊功能允许根据其与目的或搜索请求/模板的语义接近程度对所找到的信息进行排名。形成这样的度量应该考虑许多方面,从匹配概念的含义到完成这些概念的业务任务的具体情况。通常,为了构建这种相似函数,语义方法与结构方法相结合,提供概念描述的语法比较。这允许对概念进行更详细的描述,并且通过使用更具表现力的描述性逻辑来表示信息并将重点转移到语义属性上,可以显著减少语法匹配的影响。今天,DL本体是表示语义的最发达的工具,描述性逻辑(DL)的推理机制提供了逻辑推理的可能性。本文中提出的大多数估计都是基于只支持交集构造函数的基本DL,但是所描述的方法可以应用于任何提供基本推理服务的DL。本文基于描述逻辑对现有的方法、模型和措施进行了分析。从定义相似度和匹配类型两个层面对估计方法进行了分类。主要关注的是建立概念之间的相似性(概念级模型)。建立实例之间和概念与实例之间的相似性值的任务包括为实例/实例找到最具体的概念并评估概念之间的相似性。引入了存在相似性这一术语。本文给出了基于几何本体的概念和/或知识的语义相似度评价方法的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Defining degree of semantic similarity using description logic tools
Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of semantic similarity of the information allow ranking the found information on its semantic proximity to the purpose or search request/template. Forming such measures should take into account many aspects from the meanings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such similarity functions, semantic approaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.
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