From web tables to a knowledge graph: prospects of an end-to-end solution

The Web stores a large volume of web-tables with semi-structured data. The Semantic Web community considers them as a valuable source for the knowledge graph population. Interrelated named entities can be extracted from web-tables and mapped to a knowledge graph. It generally requires reconstructing the semantics missing in web-tables to interpret them according to their meaning. This paper discusses prospects of an end-to-end solution for the knowledge graph population by entities extracted from web-tables of predefined types. The discussion covers theoretical foundations both for transforming data from web-tables to entity sets (table analysis) and for mapping entities, attributes, and relations to a knowledge graph (semantic table annotation). Unlike general-purpose text mining and web-scraping tools, we aim at developing a solution that takes into account the relational nature of the information represented in web-tables. In contrast to the table-specific proposals, our approach implies both the table analysis and the semantic table annotation.