Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

• URL: http://arxiv.org/abs/2212.10915v1
• Date: Wed, 21 Dec 2022 10:54:59 GMT
• Title: Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base
• Authors: Jiakang Xu, Wolfgang Mayer, HongYu Zhang, Keqing He, Zaiwen Feng
• Abstract summary: We propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining. Our approach outperforms two state-of-theart solutions in tricky cases where only a few models are known.
• Score: 15.075047172918547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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