Constructing an ontology-based machine-readable knowledge base system from different sources with minimum human intervention, also known as ontology-based machine-readable knowledge base construction (OMRKBC), has been a long-term outstanding problem. One of the issues is how to build a large-scale OMRKBC process with appropriate structural information. To address this issue, we propose Natural Language Independent Knowledge Representation (NLIKR), a method which regards each word as a concept which should be defined by its relations with other concepts. Using NLIKR, we propose a framework for the OMRKBC process to automatically develop a comprehensive ontology-based machine-readable knowledge base system (OMRKBS) using well-built structural information. Firstly, as part of this framework, we propose formulas to discover concepts and their relations in the OMRKBS. Secondly, the challenges in obtaining rich structured information are resolved through the development of algorithms and rules. Finally, rich structured information is built in the OMRKBS. OMRKBC allows the efficient search of words and supports word queries with a specific attribute. We conduct experiments and analyze the results of relational information extraction, with the results showing that OMRKBS had an accuracy of 84% which was higher than the other knowledge base systems, namely ConceptNet, DBpedia and WordNet.

中文翻译：

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具有自然语言表达能力的综合结构化知识库系统构建

从不同的来源以最少的人工干预来构建基于本体的机器可读知识库系统，也称为基于本体的机器可读知识库构建（OMRKBC），一直是一个长期的突出问题。问题之一是如何使用适当的结构信息来构建大规模的OMRKBC流程。为了解决这个问题，我们提出了自然语言独立知识表示法（NLIKR），该方法将每个单词视为一个概念，应该通过其与其他概念的关系来定义。使用NLIKR，我们为OMRKBC流程提出了一个框架，以使用完善的结构信息自动开发基于本体的综合机器可读知识库系统（OMRKBS）。首先，作为该框架的一部分，我们提出了用于在OMRKBS中发现概念及其关系的公式。其次，通过开发算法和规则解决了获取丰富的结构化信息所面临的挑战。最后，在OMRKBS中构建了丰富的结构化信息。OMRKBC允许有效的单词搜索，并支持具有特定属性的单词查询。我们进行了实验并分析了关系信息提取的结果，结果表明OMRKBS的准确性为84％，高于其他知识库系统，即ConceptNet，DBpedia和WordNet。OMRKBC允许有效的单词搜索，并支持具有特定属性的单词查询。我们进行了实验并分析了关系信息提取的结果，结果表明OMRKBS的准确性为84％，高于其他知识库系统（即ConceptNet，DBpedia和WordNet）。OMRKBC允许有效的单词搜索，并支持具有特定属性的单词查询。我们进行了实验并分析了关系信息提取的结果，结果表明OMRKBS的准确性为84％，高于其他知识库系统（即ConceptNet，DBpedia和WordNet）。