Domain Ontology Construction using Formal Concept and Relational Concept Analysis

M. Begam
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Abstract

Domain ontology construction is an important task in knowledge management applications. Knowledge representation in appropriate form is mandate requirement using which extraction of functional facts and applying them in various business operations is feasible. Semantic web technologies are boon to the technical community that develops applications based on knowledge management. Ontology is the mean by which knowledge can be captured and queried in well-defined manner. Developing domain ontologies and investigation of domain knowledge from huge data set/corpus is the tedious task. Formal Conceptual Analysis (FCA) and Relational Concept Analysis(RCA) are data analysis methods that can be applied to domain ontology construction and information retrieval. The concept lattices generated are used for domain ontology construction. We proposed a semi-automated methodology for generating concept lattices based on FCA and RCA techniques for Tree data set. Data about the for various trees found in the world are taken into consideration and their attributes are collected from Internet. We obtain concept lattices and association rules from FCA. Modeling based on RCA has been carried out and resultant concept lattices are generated.
基于形式概念和关系概念分析的领域本体构建
领域本体构建是知识管理应用中的一项重要任务。适当形式的知识表示是一种强制性需求,使用它提取功能事实并将其应用于各种业务操作是可行的。语义web技术对于开发基于知识管理的应用程序的技术社区来说是一个福音。本体是一种能够以良好定义的方式捕获和查询知识的方法。从庞大的数据集/语料库中开发领域本体和研究领域知识是一项繁琐的任务。形式概念分析(Formal Conceptual Analysis, FCA)和关系概念分析(Relational Concept Analysis, RCA)是应用于领域本体构建和信息检索的数据分析方法。生成的概念格用于构建领域本体。针对Tree数据集,提出了一种基于FCA和RCA技术的半自动化概念格生成方法。考虑了世界上发现的各种树木的数据,并从互联网上收集了它们的属性。我们从FCA中得到概念格和关联规则。基于RCA进行了建模,生成了相应的概念格。
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