Automated Extraction of Facts from Tabular Data based on Semantic Table Annotation

The use of knowledge graphs in the construction of intelligent information and analytical systems provides to effectively structure and analyze knowledge, process large volumes of data, improve the quality of systems, and apply them in various domains such as medicine, manufacturing, trade, and finance. However, domain-specific knowledge graph engineering continues to be a difficult task, requiring the creation of specialized methods and software. One of the main trends in this area is the use of various information sources, in particular tables, which can significantly improve the efficiency of this process. This paper proposes an approach and a tool for automated extraction of specific entities (facts) from tabular data and populating them with a target knowledge graph based on the semantic interpretation (annotation) of tables. The proposed approach is implemented in the form of a special processor included in the Talisman framework. We also present an experimental evaluation of our approach and a demo of domain knowledge graph development for the Talisman framework.

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