EntailE：在常识知识图谱补全中引入文本蕴含
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
AI总结：
针对常识知识图谱稀疏性问题，提出框架EntailE，利用自然语言推理任务微调的transformer挖掘节点间文本蕴含关系以致密化图谱，并通过对比损失增强节点表示，有效提升了图谱补全性能。
摘要：
常识知识图谱补全是常识知识图谱构建和应用的一个新挑战。与Freebase和YAGO等事实知识图谱不同，常识知识图谱（CSKGs；例如ConceptNet）利用自由格式的文本将命名实体、短语和事件表示为其节点。这种松散的结构导致了庞大且稀疏的CSKGs，这使得这些节点的语义理解对于学习丰富的常识知识图谱嵌入更为关键。虽然当前方法利用语义相似性来增加图密度，但节点及其关系的语义合理性却未得到充分探索。先前的工作采用概念抽象来提高建模（事件）合理性的连贯性，但它们不够可扩展，且仍面临数据稀疏问题。在本文中，我们提出采用文本蕴含来寻找CSKG节点之间的隐式蕴含关系，以有效地致密化连接同一概念类内节点的子图，这指示了相似的合理性水平。CSKG中的每个节点使用在自然语言推理（NLI）任务上微调的transformer来寻找其top蕴含节点，从而充分捕捉文本蕴含信号。这些节点之间的蕴含关系进一步被用于：1）在源三元组和蕴含节点之间建立新连接以致密化稀疏的CSKGs；2）通过使用对比损失比较节点嵌入来丰富节点表示的泛化能力。在两个标准CSKGs上的实验表明，我们提出的框架EntailE能够提高transductive和inductive设置下CSKG补全任务的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
