Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration, federated data query and knowledge graph construction. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.

http://doi.org/10.1109/SERVICES51467.2021.00033