Relation prediction in knowledge graphs: A self-organizing neural network approach

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan
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引用次数: 0

Abstract

Knowledge graphs (KGs) in specialized domains frequently suffer from incomplete information. While current relation prediction methods for KG completion typically rely on neural network-based representation learning, we present KG2ART—a novel self-organizing neural network that employs a fundamentally different approach. KG2ART performs parallel inference over the graph structure through bidirectional interactions between bottom-up activations and top-down pattern matching to conduct relation prediction without representation learning. Our comprehensive evaluation across five diverse KGs (Nations, UMLS, Kinship, CoDEx-M, and a jet engine technical KG) demonstrates that KG2ART consistently outperforms state-of-the-art baselines (TuckER, ComplEX, RESCAL, ConvE, CompGCN) in prediction accuracy. The model achieves particularly strong results on standard benchmarks, with Hits@1 scores exceeding 90% for Nations and 60% for CoDEx-M. Remarkably, KG2ART attains these superior accuracy results while also being among the fastest models for both training and prediction across all datasets.
知识图中的关系预测:一种自组织神经网络方法
专门领域的知识图谱常常存在信息不完全的问题。虽然目前的KG完成关系预测方法通常依赖于基于神经网络的表示学习,但我们提出了kg2art——一种全新的自组织神经网络,采用了一种完全不同的方法。KG2ART通过自底向上激活和自顶向下模式匹配的双向交互,对图结构进行并行推理,在不学习表示的情况下进行关系预测。我们对五个不同的KG (Nations、UMLS、Kinship、CoDEx-M和一个喷气发动机技术KG)的综合评估表明,KG2ART在预测准确性方面始终优于最先进的基线(TuckER、ComplEX、RESCAL、ConvE、CompGCN)。该模型在标准基准上取得了特别强劲的成绩,在Hits@1上,Nations的得分超过90%,CoDEx-M的得分超过60%。值得注意的是,KG2ART获得了这些卓越的准确性结果,同时也是所有数据集中训练和预测速度最快的模型之一。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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