Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding

Rifki Afina Putri, Giwon Hong, Sung-Hyon Myaeng

Abstract
Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible than those used in a traditional Information Extraction system and a Knowledge Base (KB) such as Knowledge Graphs, the former lacks in semantics; an Open IE relation is simply a sequence of words, whereas a KB relation has a predefined meaning. As a way to provide a meaning to an Open IE relation, we attempt to align it with one of the predefined set of relations used in a KB. Our approach is to use a Siamese network that compares two sequences of word embeddings representing an Open IE relation and a predefined KB relation. In order to make the approach practical, we automatically generate a training dataset using a distant supervision approach instead of relying on a hand-labeled dataset. Our experiment shows that the proposed method can capture the relational semantics better than the recent approaches.
Anthology ID:
W19-0412
Volume:
Proceedings of the 13th International Conference on Computational Semantics - Long Papers
Month:
May
Year:
2019
Address:
Gothenburg, Sweden
Venue:
IWCS
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
142–153
Language:
URL:
https://aclanthology.org/W19-0412
DOI:
10.18653/v1/W19-0412
Bibkey:
Cite (ACL):
Rifki Afina Putri, Giwon Hong, and Sung-Hyon Myaeng. 2019. Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding. In Proceedings of the 13th International Conference on Computational Semantics - Long Papers, pages 142–153, Gothenburg, Sweden. Association for Computational Linguistics.
Cite (Informal):
Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding (Putri et al., IWCS 2019)
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PDF:
https://preview.aclanthology.org/paclic-22-ingestion/W19-0412.pdf
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