Semi-Automatic Example-Driven Linked Data Mapping Creation

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In Proceedings of the Fifth International Workshop on Linked Data for Information Extraction (2017)

Linked Data can be generated by applying mapping rules on existing (semi-)structured data. The manual creation of these rules involves a costly process for users. Therefore, (semi-)automatic approaches have been developed to assist users. Although, they provide promising results, in use cases where examples of the desired Linked Data are available they do not use the knowledge provided by these examples, resulting in Linked Data that might not be as desired. This in turn requires manual updates of the rules. These examples can in certain cases be easy to create and offer valuable knowledge relevant for the mapping process, such as which data corresponds to entities and attributes, how this data is annotated and modeled, and how different entities are linked to each other. In this paper, we introduce a semi-automatic approach to create rules based on examples for both the existing data and corresponding Linked Data. Furthermore, we made the approach available via the RMLEditor, making it readily accessible for users through a graphical user interface. The proposed approach provides a first attempt to generate a complete Linked Dataset based on user-provided examples, by creating an initial set of rules for the users.