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Towards Few-Shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-Guided Neural Process Approach

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14171))

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Abstract

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance.

Z. Zhao and L. Luo—Equal contribution.

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Acknowledgement

This research is supported by NSF of China (No: 61973162), NSF of Jiangsu Province (No: BZ2021013), NSF for Distinguished Young Scholar of Jiangsu Province (No: BK20220080), the Fundamental Research Funds for the Central Universities (Nos: 30920032202, 30921013114), CAAI-Huawei MindSpore Open Fund, and “111” Program (No: B13022).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Zicheng Zhao & Chen Gong

  2. Jiangsu Key Laboratory of Image and Video Understanding for Social Security, Nanjing, China

    Zicheng Zhao

  3. Department of Data Science and AI, Monash University, Melbourne, Australia

    Linhao Luo

  4. School of Information and Communication Technology, Griffith University, Nathan, Australia

    Shirui Pan & Quoc Viet Hung Nguyen

  5. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing, China

    Chen Gong

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  1. Zicheng Zhao
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  2. Linhao Luo
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  3. Shirui Pan
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  4. Quoc Viet Hung Nguyen
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  5. Chen Gong
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Correspondence to Chen Gong .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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In this research, we conducted experiments on publicly available datasets and implemented our approaches using commonly accepted techniques, giving utmost consideration to fairness and avoiding potential biases. We acknowledge the significance of transparency and have furnished comprehensive elucidations regarding our methodology and decision-making process. To conclude, our research adheres to ethical guidelines and poses no potential risks.

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Zhao, Z., Luo, L., Pan, S., Nguyen, Q.V.H., Gong, C. (2023). Towards Few-Shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-Guided Neural Process Approach. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_31

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  • DOI: https://doi.org/10.1007/978-3-031-43418-1_31

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