@inproceedings{putri-etal-2019-aligning,
title = "Aligning Open {IE} Relations and {KB} Relations using a {S}iamese Network Based on Word Embedding",
author = "Putri, Rifki Afina and
Hong, Giwon and
Myaeng, Sung-Hyon",
editor = "Dobnik, Simon and
Chatzikyriakidis, Stergios and
Demberg, Vera",
booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
month = may,
year = "2019",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-0412",
doi = "10.18653/v1/W19-0412",
pages = "142--153",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding
%A Putri, Rifki Afina
%A Hong, Giwon
%A Myaeng, Sung-Hyon
%Y Dobnik, Simon
%Y Chatzikyriakidis, Stergios
%Y Demberg, Vera
%S Proceedings of the 13th International Conference on Computational Semantics - Long Papers
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F putri-etal-2019-aligning
%X 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.
%R 10.18653/v1/W19-0412
%U https://aclanthology.org/W19-0412
%U https://doi.org/10.18653/v1/W19-0412
%P 142-153
Markdown (Informal)
[Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding](https://aclanthology.org/W19-0412) (Putri et al., IWCS 2019)
ACL