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Translating Representations of Knowledge Graphs with Neighbors

Published: 27 June 2018 Publication History

Abstract

Knowledge graph completion is a critical issue because many applications benefit from their structural and rich resources. In this paper, we propose a method named TransN, which consid- ers the dependencies between triples and incorporates neighbor information dynamically. In experiments, we evaluate our model by link prediction and also conduct several qualitative analyses to prove effectiveness. Experimental results show that our model could integrate neighbor information effectively and outperform state-of-the-art models.

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Cited By

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  • (2024)A Knowledge Representation Learning Model Integrating Multiple InformationProceedings of the 2024 International Conference on Image Processing, Intelligent Control and Computer Engineering10.1145/3691016.3691065(297-301)Online publication date: 19-Jul-2024
  • (2023)Visual Analytics and Deep Mining of Multidimensional Oral Health Surveys (Preprint)JMIR Medical Informatics10.2196/46275Online publication date: 5-Feb-2023
  • (2023)A Practical Approach to Constructing a Geological Knowledge Graph: A Case Study of Mineral Exploration DataJournal of Earth Science10.1007/s12583-023-1809-334:5(1374-1389)Online publication date: 18-Oct-2023
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    cover image ACM Conferences
    SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
    June 2018
    1509 pages
    ISBN:9781450356572
    DOI:10.1145/3209978
    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: 27 June 2018

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    Author Tags

    1. knowledge graph
    2. natural language processing
    3. representation learning

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    SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2024)A Knowledge Representation Learning Model Integrating Multiple InformationProceedings of the 2024 International Conference on Image Processing, Intelligent Control and Computer Engineering10.1145/3691016.3691065(297-301)Online publication date: 19-Jul-2024
    • (2023)Visual Analytics and Deep Mining of Multidimensional Oral Health Surveys (Preprint)JMIR Medical Informatics10.2196/46275Online publication date: 5-Feb-2023
    • (2023)A Practical Approach to Constructing a Geological Knowledge Graph: A Case Study of Mineral Exploration DataJournal of Earth Science10.1007/s12583-023-1809-334:5(1374-1389)Online publication date: 18-Oct-2023
    • (2022)Knowledge Base Embedding for Sampling-Based PredictionACM Transactions on Information Systems10.1145/353376941:2(1-25)Online publication date: 11-Jun-2022
    • (2021)Knowledge Graph Embedding via Metagraph LearningProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463072(2212-2216)Online publication date: 11-Jul-2021
    • (2020)PRTransE: Emphasize More Important Facts Based on Pagerank for Knowledge Graph CompletionCognitive Computing – ICCC 202010.1007/978-3-030-59585-2_2(15-26)Online publication date: 14-Sep-2020
    • (2019)Hyperlink Classification via Structured Graph EmbeddingProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331325(1017-1020)Online publication date: 18-Jul-2019

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