论文标题
结构数据源的自动语义建模，具有知识库的先验知识
Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base
论文作者
论文摘要
在线共享语义内容的关键步骤是将结构数据源映射到公共领域本体论。这个问题表示为关系到主教映射问题（Rel2onto）。需要巨大的努力和专业知识来手动对数据的语义进行建模。因此，需要一种自动学习数据源语义的方法。大多数现有工作研究源属性的语义注释。但是，尽管至关重要，但自动推断属性之间关系的研究非常有限。在本文中，我们提出了一种新颖的方法，用于使用机器学习，图形匹配和修改的频繁次级挖掘来修改候选模型的语义注释结构化数据源。在我们的工作中，知识图被用作先验知识。我们的评估表明，在棘手的情况下，我们的方法的表现优于两种最先进的解决方案，在棘手的情况下，只有几个语义模型。
A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.
