About: Attribute-Driven Capsule Network for Entity Relation Prediction

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Attribute-Driven Capsule Network for Entity Relation Prediction
Creator	Chen, Xi Chen, Jiayin Gong, Xiaolong Ma, Zhiyi
topic	covid:a1eb118c77f881533ee999a1d5f4404a009f27dc#this
source	PMC
abstract	Multi-attribute entity relation prediction is a novel data mining application about designing an intelligent system that supports inferencing across attributes information. However, most existing deep learning methods capture the inner structural information between different attributes are far more limited. In this paper, we propose an attribute-driven approach for entity relation prediction task based on capsule networks that have been shown to demonstrate good performance on relation mining. We develop a self-attention routing method to encapsulate multiple attributes semantic representation into relational semantic capsules and using dynamic routing method to generate class capsules for predicting relations. Due to the lack of multi-attribute entity relation data is a major obstacle in this task, we construct a new real-world multi-attribute entity relation dataset in this work. Experimental results show significant superiority of our model, as compared with other baselines.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47426-3_52
has license	no-cc
sha1sum (hex)	a1eb118c77f881533ee999a1d5f4404a009f27dc
schema:url	https://doi.org/10.1007/978-3-030-47426-3_52
resource representing a document's title	Attribute-Driven Capsule Network for Entity Relation Prediction
has PubMed Central identifier	PMC7206169
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:a1eb118c77f881533ee999a1d5f4404a009f27dc#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/a1eb118c77f881533ee999a1d5f4404a009f27dc
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