从大规模链接的开放数据中提取特定于领域的概念

我们提出了一种从大规模链接开放数据(LOD)中提取目标域概念的方法，以支持领域本体的构建，提供特定领域的知识和定义。该方法通过将LOD词汇表与与目标域相关的技术术语联系起来来定义搜索实体。然后将搜索实体作为获取LOD中上层概念的起点，并检查公共上层实体的出现和路径链关系，以确定目标域中概念连接的范围。利用技术字典索引和自然语言处理来评估所提取的概念是否覆盖该领域。作为从LOD中提取类层次结构的一个例子，我们使用Wikidata构建了聚合物材料和物理性质的领域本体。该方法可应用于具有类层次结构的一般数据集，并允许本体开发人员为其自己的目的创建领域本体的初始模型。

原文题目：Extracting Domain-specific Concepts from Large-scale Linked Open Data

原文：We propose a methodology for extracting concepts for a target domain from large-scale linked open data (LOD) to support the construction of domain ontologies providing field-specific knowledge and definitions. The proposed method defines search entities by linking the LOD vocabulary with technical terms related to the target domain. The search entities are then used as a starting point for obtaining upper-level concepts in the LOD, and the occurrences of common upper-level entities and the chain-of-path relationships are examined to determine the range of conceptual connections in the target domain. A technical dictionary index and natural language processing are used to evaluate whether the extracted concepts cover the domain. As an example of extracting a class hierarchy from LOD, we used Wikidata to construct a domain ontology for polymer materials and physical properties. The proposed method can be applied to general datasets with class hierarchies, and it allows ontology developers to create an initial model of the domain ontology for their own purposes.