Presentation | 2020-03-11 Knowledge Graph Completion by Separating Transition and Score Functions Kenta Hama, Takashi Matsubara, Kuniaki Uehara, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(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) |
Keyword(in English) | Knowledge Graph / Link Prediction / Knowledge Graph Embedding |
Paper # | IBISML2019-41 |
Date of Issue | 2020-03-03 (IBISML) |
Conference Information | |
Committee | IBISML |
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Conference Date | 2020/3/10(2days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Kyoto University |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Machine learning, etc. |
Chair | Hisashi Kashima(Kyoto Univ.) |
Vice Chair | Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
Secretary | Masashi Sugiyama(Nagoya Inst. of Tech.) / Koji Tsuda(AIST) |
Assistant | Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.) |
Paper Information | |
Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
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Language | JPN |
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 |
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.) |
Date | 2020-03-11 |
Paper # | IBISML2019-41 |
Volume (vol) | vol.119 |
Number (no) | IBISML-476 |
Page | pp.pp.59-62(IBISML), |
#Pages | 4 |
Date of Issue | 2020-03-03 (IBISML) |