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
Venues:
LSDSem | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–24
Language:
URL:
https://aclanthology.org/W17-0902
DOI:
10.18653/v1/W17-0902
Bibkey:
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., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-0902.pdf
Code
 vered1986/OKR
Data
DBpediaECB+QA-SRL