19/10/2020

Neural relation extraction on wikipedia tables for augmenting knowledge graphs

Erin Macdonald, Denilson Barbosa

Keywords: information extraction, benchmarking, web tables

Abstract Paper Similar Papers

Abstract: Knowledge Graph Augmentation is the task of adding missing facts to an incomplete knowledge graph to improve its effectiveness in applications such as web search and question answering. State-of-the-art methods rely on information extraction from running text, leaving rich sources of facts such as tables behind. We help close this gap with a neural method that uses contextual information surrounding a table in a Wikipedia article to extract relations between entities appearing in the same row of a table or between the entity of said article and entities appearing in the table. We trained and tested our method on a much larger dataset compared to previous work which we have made public and observed experimentally that our method is very promising for the task.

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