{"title":"Relation prediction in knowledge graphs: A self-organizing neural network approach","authors":"Budhitama Subagdja, D. Shanthoshigaa, Ah-Hwee Tan","doi":"10.1016/j.neunet.2025.107679","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107679"},"PeriodicalIF":6.0000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025005593","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.