research-article

HARP: A Novel Hierarchical Attention Model for Relation Prediction

Authors:

Huanhuan ZhangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 2

Article No.: 17, Pages 1 - 22

https://doi.org/10.1145/3424673

Published: 04 January 2021 Publication History

Abstract

Recent years have witnessed great advancement of representation learning (RL)-based models for the knowledge graph relation prediction task. However, they generally rely on structure information embedded in the encyclopedic knowledge graph, while the beneficial semantic information provided by lexical knowledge graph is ignored, leading the problem of shallow understanding and coarse-grained analysis for knowledge acquisition. Therefore, this article introduces concept information derived from the lexical knowledge graph (e.g., Probase), and proposes a novel Hierarchical Attention model for Relation Prediction, which consists of entity-level attention mechanism and concept-level attention mechanism, to throughly integrate multiple semantic signals. Experimental results demonstrate the efficiency of the proposed method on two benchmark datasets.

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Index Terms

HARP: A Novel Hierarchical Attention Model for Relation Prediction
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 2

Survey Paper and Regular Papers

April 2021

524 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3446665

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Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 04 January 2021

Accepted: 01 September 2020

Revised: 01 July 2020

Received: 01 February 2020

Published in TKDD Volume 15, Issue 2

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National Integrated Big Data Center Pilot Project
China Postdoctoral Science Foundation
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Joint Advanced Research Foundation of China Electronics Technology Group Corporation (CETC)

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Wang JWang BGao JHu YYin B(2023)Multi-Concept Representation Learning for Knowledge Graph CompletionACM Transactions on Knowledge Discovery from Data10.1145/353301717:1(1-19)Online publication date: 20-Feb-2023
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