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Relation-Wise Automatic Domain-Range Information Management for Knowledge Entries

机译:知识条目的关系型自动域范围信息管理

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Relations play a vital role on knowledge construction and maintenance thereof. They for example connect domain type entities to range type entities, like the relation born in connects some Persons to some Places. Over any dataset, the domain-range information is used to maintain data consistency. Therefore, we see that knowledge construction frameworks sometime engage costly Knowledge Engineers to define the domain-range information in form of a schema or an ontology. We also see that frameworks that hold such defined domain-range information, often do not follow them strictly. In the worst case some frameworks do not even allow to define a domain-range, rather they just gather the knowledge entries. One reason of not defining the domain-range information is that it is costly. On the other hand, the reason for not following the domain-range constraint is that the most of them are either manual or semi-automatic, therefore they face adaptation difficulty. In this research, we propose a relation-wise machine learning model that can define and validate domain-range information automatically. The initial experiment shows that the proposed framework performs promisingly.
机译:关系在知识的构建和维护中起着至关重要的作用。例如,它们将域类型实体连接到范围类型实体,就像出生于的关系将某些人员连接到某些位置。在任何数据集上,域范围信息都用于维护数据一致性。因此,我们看到知识构建框架有时会聘请昂贵的知识工程师来以架构或本体的形式定义域范围信息。我们还看到,拥有此类定义的范围信息的框架通常不会严格遵循它们。在最坏的情况下,某些框架甚至不允许定义域范围,而只是收集知识条目。不定义域范围信息的原因之一是它的成本很高。另一方面,不遵循域范围约束的原因是它们大多数是手动或半自动的,因此它们面临适应困难。在这项研究中,我们提出了一种可以自动定义和验证域范围信息的基于关系的机器学习模型。初步实验表明,所提出的框架具有令人满意的性能。

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