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1
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Isabelle, Pierre
;
Bender, Emily M.
;
Schütze, Hinrich
;
Derczynski, Leon
;
Yin, Wenpeng
;
Yaghoobzadeh, Yadollah
. - : Ludwig-Maximilians-Universität München, 2018
Abstract:
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two enti- ties connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering
Keyword:
ddc:000
;
ddc:004
;
ddc:400
;
ddc:410
URL:
https://epub.ub.uni-muenchen.de/61860/1/C18-1200.pdf
https://doi.org/10.5282/ubm/epub.61860
http://nbn-resolving.de/urn:nbn:de:bvb:19-epub-61860-0
https://epub.ub.uni-muenchen.de/61860/
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