Communicative Message Passing for Inductive Relation Reasoning

• URL: http://arxiv.org/abs/2012.08911v1
• Date: Wed, 16 Dec 2020 12:42:06 GMT
• Title: Communicative Message Passing for Inductive Relation Reasoning
• Authors: Sijie Mai, Shuangjia Zheng, Yuedong Yang, Haifeng Hu
• Abstract summary: We introduce textbfCtextbfommunicative textbfMessage textbfPassing neural network for textbfInductive retextbfLation rtextbfEasoning textbfCoMPILE. In contrast to existing models, CoMPILE strengthens the message interactions between edges and entitles through a communicative kernel.
• Score: 17.380798747650783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relation prediction for knowledge graphs aims at predicting missing relationships between entities. Despite the importance of inductive relation prediction, most previous works are limited to a transductive setting and cannot process previously unseen entities. The recent proposed subgraph-based relation reasoning models provided alternatives to predict links from the subgraph structure surrounding a candidate triplet inductively. However, we observe that these methods often neglect the directed nature of the extracted subgraph and weaken the role of relation information in the subgraph modeling. As a result, they fail to effectively handle the asymmetric/anti-symmetric triplets and produce insufficient embeddings for the target triplets. To this end, we introduce a \textbf{C}\textbf{o}mmunicative \textbf{M}essage \textbf{P}assing neural network for \textbf{I}nductive re\textbf{L}ation r\textbf{E}asoning, \textbf{CoMPILE}, that reasons over local directed subgraph structures and has a vigorous inductive bias to process entity-independent semantic relations. In contrast to existing models, CoMPILE strengthens the message interactions between edges and entitles through a communicative kernel and enables a sufficient flow of relation information. Moreover, we demonstrate that CoMPILE can naturally handle asymmetric/anti-symmetric relations without the need for explosively increasing the number of model parameters by extracting the directed enclosing subgraphs. Extensive experiments show substantial performance gains in comparison to state-of-the-art methods on commonly used benchmark datasets with variant inductive settings.

Related papers

• Revealing Decurve Flows for Generalized Graph Propagation [108.80758541147418]
  This study addresses the limitations of the traditional analysis of message-passing, central to graph learning, by defining em textbfgeneralized propagation with directed and weighted graphs. We include a preliminary exploration of learned propagation patterns in datasets, a first in the field.
  arXiv  Detail & Related papers  (2024-02-13T14:13:17Z)
• Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction [19.019881161010474]
  Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM) Existing approaches therefore solve the problem in an indirect way: they fine-tune an LM to learn embeddings of the head and tail entities, and then predict the relationship from these entity embeddings. Our hypothesis in this paper is that relation extraction models can be improved by capturing relationships in a more direct way.
  arXiv  Detail & Related papers  (2023-12-18T09:58:19Z)
• Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction [121.65152276851619]
  We show that semantic correlations between relations are inherently edge-level and entity-independent. We propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph.
  arXiv  Detail & Related papers  (2023-09-20T08:11:58Z)
• Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach [49.00753238429618]
  Few-shot inductive link prediction on knowledge graphs aims to predict missing links for unseen entities with few-shot links observed. Recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. We propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP.
  arXiv  Detail & Related papers  (2023-06-26T12:02:32Z)
• Message Intercommunication for Inductive Relation Reasoning [49.731293143079455]
  We develop a novel inductive relation reasoning model called MINES. We introduce a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Our experiments show that MINES outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-23T13:51:46Z)
• Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction [30.404149577013595]
  We introduce Relation-dependent Contrastive Learning (ReCoLe) for inductive relation prediction. GNN-based encoder is optimized by contrastive learning, which ensures satisfactory performance on long-tail relations. Experimental results suggest that ReCoLe outperforms state-of-the-art methods on commonly used inductive datasets.
  arXiv  Detail & Related papers  (2022-11-22T13:30:49Z)
• Relational Message Passing for Fully Inductive Knowledge Graph Completion [37.29833710603933]
  In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. We propose a new method named RMPI which uses a novel Message Passing network for fully available KGC.
  arXiv  Detail & Related papers  (2022-10-08T10:35:52Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Link Prediction on N-ary Relational Data Based on Relatedness Evaluation [61.61555159755858]
  We propose a method called NaLP to conduct link prediction on n-ary relational data. We represent each n-ary relational fact as a set of its role and role-value pairs. Experimental results validate the effectiveness and merits of the proposed methods.
  arXiv  Detail & Related papers  (2021-04-21T09:06:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.