Automatic semantic modeling for structural data source with the prior knowledge from knowledge graph
Date
2021
Authors
Feng, Z.W.
Xu, J.K.
Mayer, W.
Huang, W.Y.
He, K.Q.
Stumptner, M.
Grossmann, G.
Zhang, H.Y.
Ling, L.
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Conference paper
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Proceedings 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021, 2021, pp.2034-2041
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2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 (20 Dec 2021 - 22 Dec 2021 : Haikou, China)
Abstract
Mapping structured data to a shared domain ontology is a key step in publishing semantic content on the Web. This problem is known as Relational-To-Ontology Mapping Problem (Rel2Onto). Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Most of the related work focuses on semantic annotation of source attributes. However, besides semantically annotating source attributes, it is challenging to explicitly infer the relationships between attributes. In this paper we improve previous work by Taheriyan et al. [4] using Subgraph Matching to take into account frequencies of candidate semantic models occurring in the domain knowledge graph used as background knowledge. Preliminary experiments demonstrate that our method achieves higher precision and recall than the state-of-the-art solutions in the difficult scenarios where only few historical mappings between domain ontology and data sources are available.
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Copyright 2021 IEEE