EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion

• URL: http://arxiv.org/abs/2402.09666v1
• Date: Thu, 15 Feb 2024 02:27:23 GMT
• Title: EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
• Authors: Ying Su, Tianqing Fang, Huiru Xiao, Weiqi Wang, Yangqiu Song, Tong Zhang, Lei Chen
• Abstract summary: Commonsense knowledge graphs (CSKGs) utilize free-form text to represent named entities, short phrases, and events as their nodes. Current methods leverage semantic similarities to increase the graph density, but the semantic plausibility of the nodes and their relations are under-explored. We propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class.
• Score: 54.12709176438264
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Commonsense knowledge graph completion is a new challenge for commonsense knowledge graph construction and application. In contrast to factual knowledge graphs such as Freebase and YAGO, commonsense knowledge graphs (CSKGs; e.g., ConceptNet) utilize free-form text to represent named entities, short phrases, and events as their nodes. Such a loose structure results in large and sparse CSKGs, which makes the semantic understanding of these nodes more critical for learning rich commonsense knowledge graph embedding. While current methods leverage semantic similarities to increase the graph density, the semantic plausibility of the nodes and their relations are under-explored. Previous works adopt conceptual abstraction to improve the consistency of modeling (event) plausibility, but they are not scalable enough and still suffer from data sparsity. In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility. Each node in CSKG finds its top entailed nodes using a finetuned transformer over natural language inference (NLI) tasks, which sufficiently capture textual entailment signals. The entailment relation between these nodes are further utilized to: 1) build new connections between source triplets and entailed nodes to densify the sparse CSKGs; 2) enrich the generalization ability of node representations by comparing the node embeddings with a contrastive loss. Experiments on two standard CSKGs demonstrate that our proposed framework EntailE can improve the performance of CSKG completion tasks under both transductive and inductive settings.

Related papers

• Multi-Evidence based Fact Verification via A Confidential Graph Neural Network [20.574234947055494]
  We introduce a Confidential Graph Attention Network (CO-GAT) to mitigate the propagation of noisy semantic information. CO-GAT achieves a 73.59% FEVER score on the FEVER dataset and shows the ability generalization by broadening the effectiveness.
  arXiv  Detail & Related papers  (2024-05-17T01:02:03Z)
• GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy [21.553180564868306]
  GraphRARE is a framework built upon node relative entropy and deep reinforcement learning. An innovative node relative entropy is used to measure mutual information between node pairs. A deep reinforcement learning-based algorithm is developed to optimize the graph topology.
  arXiv  Detail & Related papers  (2023-12-15T11:30:18Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering [14.854971279160933]
  We propose a new CSKG completion framework based on Contrastive Pretraining and Node Clustering. We evaluate our CPNC approach on two CSKG completion benchmarks (CN-100K and ATOMIC), where CPNC outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-26T15:24:32Z)
• Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical [85.0376670244522]
  We investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. We develop a Star-shaped Reasoning Graph Network (S-RGN) to exploit more powerful and flexible capacity. We also develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity.
  arXiv  Detail & Related papers  (2021-09-06T03:16:56Z)
• Towards Self-Explainable Graph Neural Network [24.18369781999988]
  Graph Neural Networks (GNNs) generalize the deep neural networks to graph-structured data. GNNs lack explainability, which limits their adoption in scenarios that demand the transparency of models. We propose a new framework which can find $K$-nearest labeled nodes for each unlabeled node to give explainable node classification.
  arXiv  Detail & Related papers  (2021-08-26T22:45:11Z)
• Pair-view Unsupervised Graph Representation Learning [2.8650714782703366]
  Low-dimension graph embeddings have proved extremely useful in various downstream tasks in large graphs. This paper pro-poses PairE, a solution to use "pair", a higher level unit than a "node" as the core for graph embeddings. Experiment results show that PairE consistently outperforms the state of baselines in all four downstream tasks.
  arXiv  Detail & Related papers  (2020-12-11T04:09:47Z)
• Factorizable Graph Convolutional Networks [90.59836684458905]
  We introduce a novel graph convolutional network (GCN) that explicitly disentangles intertwined relations encoded in a graph. FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs. We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-10-12T03:01:40Z)
• Iterative Context-Aware Graph Inference for Visual Dialog [126.016187323249]
  We propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations.
  arXiv  Detail & Related papers  (2020-04-05T13:09:37Z)
• Graph Inference Learning for Semi-supervised Classification [50.55765399527556]
  We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification. For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes. Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-17T02:52:30Z)

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.