A Consolidated Open Knowledge Representation for Multiple Texts
Rachel Wities, Vered Shwartz, Gabriel Stanovsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martinez Camara, Iryna Gurevych, Ido Dagan
Abstract
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions using entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to give semantic applications an easy handle on consolidated information across multiple texts.- Anthology ID:
- W17-0902
- Volume:
- Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Michael Roth, Nasrin Mostafazadeh, Nathanael Chambers, Annie Louis
- Venue:
- LSDSem
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–24
- Language:
- URL:
- https://aclanthology.org/W17-0902
- DOI:
- 10.18653/v1/W17-0902
- Cite (ACL):
- Rachel Wities, Vered Shwartz, Gabriel Stanovsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martinez Camara, Iryna Gurevych, and Ido Dagan. 2017. A Consolidated Open Knowledge Representation for Multiple Texts. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 12–24, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- A Consolidated Open Knowledge Representation for Multiple Texts (Wities et al., LSDSem 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W17-0902.pdf
- Code
- vered1986/OKR
- Data
- DBpedia, ECB+, QA-SRL