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

Jiakang Xu, Wolfgang Mayer, Hongyu Zhang (), Keqing He and Zaiwen Feng ()
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Jiakang Xu: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
Wolfgang Mayer: Industrial AI Research Centre, University of South Australia, Mawson Lakes, SA 5095, Australia
Hongyu Zhang: Hubei HongShan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
Keqing He: School of Computer, Wuhan University, Wuhan 430072, China
Zaiwen Feng: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China

Mathematics, 2022, vol. 10, issue 24, 1-19

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.

Keywords: semantic model; frequent subgraph mining; knowledge graph; ontology (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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