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When True Becomes False: Few-Shot Link Prediction beyond Binary Relations through Mining False Positive Entities

Published: 10 October 2022 Publication History

Abstract

Recently, the link prediction task on Hyper-relational Knowledge Graphs (HKGs) has been a hot spot, which aims to predict new facts beyond binary relations. Although previous models have accomplished considerable achievements, there remain three challenges: i) the previous models neglect the existence of False Positive Entities (FPEs), which are true entities in the binary triples, yet becomes false when encountering the query statements of HKGs; ii) Due to the sparse interactions, the models are not capable of coping with long-tail hyper-relations, which are ubiquitous in the real-world; iii) The models are generally transductive learning processes, and have difficulty in adapting new hyper-relations. To tackle the above issues, we firstly propose the task of few-shot link prediction on HKGs and devise hyper-relation-aware attention networks with a contrastive loss, which are empowered to encode all entities including FPEs effectively and increase the distance between the true entities and FPEs through contrastive learning. With few-shot references available, the proposed model then learns the representations of their long-tail hyper-relations and predicts new links by calculating the likelihood between queries and references. Furthermore, our model is inductive and can be scalable to any new hyper-relation effortlessly. Since it is the first trial on few-shot link prediction for HKGs, we also modify the existing few-shot learning approaches on binary relational data to work with HKGs as baselines. Experimental results on three real-world datasets show the superiority of our model over various state-of-the-art baselines.

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cover image ACM Conferences
MM '22: Proceedings of the 30th ACM International Conference on Multimedia
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
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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Published: 10 October 2022

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Author Tags

  1. attention networks
  2. contrastive learning
  3. false positive entities
  4. few-shot learning
  5. hyper-relational knowledge graphs
  6. n-ary facts

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  • Opening Project of State Key Laboratory of Digital Publishing Technology of Founder Group
  • National Natural Science Foundation of China
  • National Social Science Foundation of China
  • Research Seed Funds of School of Interdisciplinary Studies of Renmin University of China

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