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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P17-2048.pdf