MHEC: One-shot relational learning of knowledge graphs completion based on multi-hop information enhancement

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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Abstract

With the wide application of knowledge graphs, knowledge graph completion has garnered increasing attention in recent years. However, we find that the long tail relation is more common in the KG. These relations typically do not have a large number of triples for training and are referred to as few-shot relations. The knowledge graph completion in the few-shot scenario is a major challenge currently. The current mainstream knowledge graph completion algorithms have the following drawbacks. The metric-based methods lack interpretability of results, while the algorithms based on path interaction are not suitable for few-shot scenarios and the availability of the model is limited in sparse knowledge graphs. In this paper, we propose a one-shot relational learning of knowledge graphs completion based on multi-hop information enhancement(MHEC). Firstly, MHEC extracts entity concepts from multi-hop paths to obtain task related entity concepts and filters out noisy neighbor attributes. Then, MHEC combines multi-hop path information between head and tail to represent entity pairs. Compared to previous completion methods that only consider structural features of entities, MHEC considers the reasoning logic between entity pairs, which not only includes structural features but also possesses rich semantic features. Next, MHEC introduces a reasoning process in the completion task to address the issues of lack of interpretability in the one-shot scenario. In addition, to improve completion and reasoning quality in sparse knowledge graphs, MHEC utilizes contrastive learning to enhance pre-training vector representations of entities and relations and proposes a matching processor that leverages the semantic information of pre-training vectors to assist the reasoning model in expanding the multi-hop paths. Experiments demonstrate that MHEC outperforms the state-of-the-art completion techniques on real-world datasets NELL-One and FB15k237-One.
MHEC:基于多跳信息增强的知识图谱完成的一次性关系学习
近年来,随着知识图谱的广泛应用,知识图谱补全越来越受到关注。然而,我们发现长尾关系在知识图谱中更为常见。这些关系通常没有大量的三元组可供训练,被称为 "少量关系"。在少量关系的情况下完成知识图谱是当前的一大挑战。目前主流的知识图谱补全算法有以下缺点。基于度量的方法缺乏结果的可解释性,而基于路径交互的算法不适合少点场景,在稀疏知识图谱中模型的可用性有限。在本文中,我们提出了一种基于多跳信息增强(MHEC)的知识图完成的一次性关系学习方法。首先,MHEC 从多跳路径中提取实体概念,得到与任务相关的实体概念,并过滤掉有噪声的邻居属性。然后,MHEC 结合头尾之间的多跳路径信息来表示实体对。与以往只考虑实体结构特征的完成方法相比,MHEC 考虑了实体对之间的推理逻辑,这不仅包括结构特征,还具有丰富的语义特征。接下来,MHEC 在完成任务中引入了推理过程,以解决单次场景中缺乏可解释性的问题。此外,为了提高稀疏知识图谱的完成和推理质量,MHEC利用对比学习来增强实体和关系的预训练向量表示,并提出了一种匹配处理器,利用预训练向量的语义信息来辅助推理模型扩展多跳路径。实验证明,在实际数据集 NELL-One 和 FB15k237-One 上,MHEC 的表现优于最先进的补全技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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