Development of domain knowledge graph based on semantic annotation of tabular data

The article outlines an approach and software tool for the automated enrichment of domain-oriented knowledge graphs with new facts derived from semantically annotated tabular data. For semantic annotation of table columns, a combination of three heuristic methods is proposed, leveraging named entity recognition in cells, lexical matching, and feature grouping. This approach is implemented as a dedicated handler within the Talisman software platform. An example and experimental evaluation of the approach during the semantic annotation of columns are provided using a test set of tabular data across six thematic categories: "organization employees," "open vacancies," "car model market," "famous scientists," "book sales," and "tennis player rankings." Evaluation metrics included precision, recall, and F-measure, with final results across all six categories as follows: precision - 79%, recall- 63%, F-measure - 70%. These results highlight the potential of the developed approach for enriching domain-oriented knowledge graphs with new facts from semantically annotated tabular data. The limitations of the proposed approach are also discussedfrom semantically annotated tabular data. The paper also provides a number of limitations of the proposed approach.