面向时序知识图谱表示学习的统一评估基准OA

Temporal knowledge graph representation learning(TKGR-LE)aims to embed dynamic facts and entity rela-tionships into a low-dimensional vector space to capture the semantic evolution along the time dimension.This technique is widely applied in various scenarios,such as financial risk control,recommender systems,and event prediction.However,despite its promising outlook,the development of this field is significantly hampered by a profound lack of standardiza-tion.Existing studies often focus exclusively on the construction and validation of a single model,resulting in a deficit of a unified,reproducible experimental framework and a set of objective,standardized evaluation protocols.This"siloed"approach severely limits the performance comparison and technical exchange among different models,greatly constraining the overall progress and rapid iteration of algorithms in this domain.To address this critical bottleneck,this paper proposes a unified framework for temporal knowledge graph representation learning.The core objective of this framework is to establish a standardized system for research,development,and evaluation.Utilizing a modular design paradigm,it stan-dardizes the entire workflow,encompassing model design,implementation,and evaluation,and constructs an integrated experimental platform.Specifically,this paper systematically reproduces and optimizes eight categories of temporal algo-rithms,including TTransE and TA-DistMult.This paper conducts temporal-aware link prediction experiments on standard benchmark datasets,such as ICEWS and YAGO.Furthermore,multi-dimensional comparative experiments are performed to validate the advantages of this framework in algorithmic compatibility,evaluation objectivity,and scenario universality.This research endeavors to establish a standardized R&D and evaluation system for temporal knowledge graphs.By intro-ducing a normalized performance comparison mechanism,it provides a crucial baseline framework to facilitate future algorithmic iteration and technological innovation.