Name
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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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3340531.3412164 | CIKM '20: The 29th ACM International Conference on Information and Knowledge Management
Virtual Event
Ireland
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-6859-9 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erin Macdonald | 1 | 1 | 0.35 |
| Denilson Barbosa | 2 | 610 | 43.52 |