Pocket Knowledge Base Population

Travis Wolfe, Mark Dredze, Benjamin Van Durme


Abstract
Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs. We propose Pocket Knowledge Base Population (PKBP), the task of dynamically constructing a KB of entities related to a query and finding the best characterization of relationships between entities. We describe novel Open Information Extraction methods which leverage the PKB to find informative trigger words. We evaluate using existing KBP shared-task data as well anew annotations collected for this work. Our methods produce high quality KB from just text with many more entities and relationships than existing KBP systems.
Anthology ID:
P17-2048
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
305–310
Language:
URL:
https://aclanthology.org/P17-2048
DOI:
10.18653/v1/P17-2048
Bibkey:
Cite (ACL):
Travis Wolfe, Mark Dredze, and Benjamin Van Durme. 2017. Pocket Knowledge Base Population. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 305–310, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Pocket Knowledge Base Population (Wolfe et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2048.pdf