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MultiE: Multi-Task Embedding for Knowledge Base Completion

Published: 17 October 2018 Publication History

Abstract

Completing knowledge bases (KBs) with missing facts is of great importance, since most existing KBs are far from complete. To this end, many knowledge base completion (KBC) methods have been proposed. However, most existing methods embed each relation into a vector separately, while ignoring the correlations among different relations. Actually, in large-scale KBs, there always exist some relations that are semantically related, and we believe this can help to facilitate the knowledge sharing when learning the embedding of related relations simultaneously. Along this line, we propose a novel KBC model by Multi -Task E mbedding, named MultiE. In this model, semantically related relations are first clustered into the same group, and then learning the embedding of each relation can leverage the knowledge among different relations. Moreover, we propose a three-layer network to predict the missing values of incomplete knowledge triples. Finally, experiments on three popular benchmarks FB15k, FB15k-237 and WN18 are conducted to demonstrate the effectiveness of MultiE against some state-of-the-art baseline competitors.

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Cited By

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  • (2024)GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relationsInternational Journal of Geographical Information Science10.1080/13658816.2024.241273139:2(376-399)Online publication date: 16-Oct-2024
  • (2023)Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312795135:4(4321-4334)Online publication date: 1-Apr-2023
  • (2020)Modeling relation paths for knowledge base completion via joint adversarial trainingKnowledge-Based Systems10.1016/j.knosys.2020.105865(105865)Online publication date: May-2020

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  1. MultiE: Multi-Task Embedding for Knowledge Base Completion

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    cover image ACM Conferences
    CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
    October 2018
    2362 pages
    ISBN:9781450360142
    DOI:10.1145/3269206
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 17 October 2018

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    Author Tags

    1. embedding
    2. knowledge base completion
    3. multi-task learning

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    Funding Sources

    • National Natural Science Foundation of China
    • National Key Research and Development Program of China
    • Guangdong provincial science and technology plan projects
    • Project of Youth Innovation Promotion Association CAS

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    CIKM '18
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    CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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    Cited By

    View all
    • (2024)GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relationsInternational Journal of Geographical Information Science10.1080/13658816.2024.241273139:2(376-399)Online publication date: 16-Oct-2024
    • (2023)Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.312795135:4(4321-4334)Online publication date: 1-Apr-2023
    • (2020)Modeling relation paths for knowledge base completion via joint adversarial trainingKnowledge-Based Systems10.1016/j.knosys.2020.105865(105865)Online publication date: May-2020

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