About: Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.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	Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
Creator	Hildebrandt, Marcel Joblin, Mitchell Liu, Yushan Ringsquandl, Martin Tresp, Volker
source	ArXiv
abstract	The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.
has issue date	2020-07-10(xsd:dateTime)
has license	arxiv
sha1sum (hex)	589bf79968b1a789ccf883c74bf42c92c570212e
resource representing a document's title	Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
resource representing a document's body	covid:589bf79968b1a789ccf883c74bf42c92c570212e#body_text
is schema:about of	named entity 'biomedical' named entity 'dependencies' named entity 'PROBLEM' named entity 'APPROACH' named entity 'DATA' »more»

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