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Paper Abstract and Keywords
Presentation 2020-03-11 09:45
Knowledge Graph Completion by Separating Transition and Score Functions
Kenta Hama, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.) IBISML2019-41
Abstract (in Japanese) (See Japanese page) 
(in English) A knowledge graph is represented by a set of two entities and the relations, and used for various tasks such as information extraction, question answering, and sentence understanding. Since many knowledge graphs include missing relations and entities, Knowledge Graph Completion (KGC) is important to use for other tasks. Translation-based Models can predict missing entities by learning transition functions and embeddings of entities. However, many of these models are known that the accuracy of prediction is reduced in tasks such as Path Query Answering (PQA), which makes multiple transitions. On the other hand, if the transition embedding point is matched with the correct embedding point in order to prevent the loss of prediction accuracy, the embedding point having a one-to-many relationship will be collapsed into one point. In this study, we tried to solve the above problem by defining a transition function and an evaluation function of transition points separately in translation-based models. The proposed method improved accuracy in PQA and KGC.
Keyword (in Japanese) (See Japanese page) 
(in English) Knowledge Graph / Link Prediction / Knowledge Graph Embedding / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 476, IBISML2019-41, pp. 59-62, March 2020.
Paper # IBISML2019-41 
Date of Issue 2020-03-03 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2019-41

Conference Information
Committee IBISML  
Conference Date 2020-03-10 - 2020-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2020-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Knowledge Graph Completion by Separating Transition and Score Functions 
Sub Title (in English)  
Keyword(1) Knowledge Graph  
Keyword(2) Link Prediction  
Keyword(3) Knowledge Graph Embedding  
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1st Author's Name Kenta Hama  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Takashi Matsubara  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Kuniaki Uehara  
3rd Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2020-03-11 09:45:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2019-41 
Volume (vol) vol.119 
Number (no) no.476 
Page pp.59-62 
#Pages 4 
Date of Issue 2020-03-03 (IBISML) 

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