面向GML的知识图谱平台
Towards a GML-Enabled Knowledge Graph Platform
摘要：
本愿景论文提出了KGNet，这是一种按需图机器学习（GML）作为RDF引擎之上的服务，以支持支持GML的SPARQL查询。KGNet通过识别特定于任务的子图，在KG上自动训练GML模型。这有助于减少与任务无关的KG结构和属性，以获得更好的可扩展性和准确性。当在KG上训练GML模型时，KGNet以RDF图的形式收集训练模型的元数据，称为KGMeta，它与KG中的相关子图互连。最后，所有经过训练的模型都可以通过类似SPARQL的查询访问。我们称之为启用GML的查询，并将其称为SPARQLML。KGNet在现有的RDF引擎之上支持SPARQLML，作为使用GML模型在KG上查询和推理的接口。KGNet的开发在多个领域带来了研究机会，包括用于识别特定于任务的子图的元采样、具有计算约束（例如有限的时间和内存预算）的GML管道自动化以及SPARQLML查询优化。KGNet支持不同的GML任务，例如节点分类、链接预测和语义实体匹配。我们使用两个不同应用领域的真实KG对KGNet进行了评估。与在整个KG上进行训练相比，KGNet显著减少了训练时间和内存使用，同时保持了相当或更高的准确性。KGNet源代码可供进一步研究
英文摘要：
This vision paper proposes KGNet, an on-demand graph machine learning (GML) as a service on top of RDF engines to support GML-enabled SPARQL queries. KGNet automates the training of GML models on a KG by identifying a task-specific subgraph. This helps reduce the task-irrelevant KG structure and properties for better scalability and accuracy. While training a GML model on KG, KGNet collects metadata of trained models in the form of an RDF graph called KGMeta, which is interlinked with the relevant subgraphs in KG. Finally, all trained models are accessible via a SPARQL-like query. We call it a GML-enabled query and refer to it as SPARQLML. KGNet supports SPARQLML on top of existing RDF engines as an interface for querying and inferencing over KGs using GML models. The development of KGNet poses research opportunities in several areas, including meta-sampling for identifying task-specific subgraphs, GML pipeline automation with computational constraints, such as limited time and memory budget, and SPARQLML query optimization. KGNet supports different GML tasks, such as node classification, link prediction, and semantic entity matching. We evaluated KGNet using two real KGs of different application domains. Compared to training on the entire KG, KGNet significantly reduced training time and memory usage while maintaining comparable or improved accuracy. The KGNet source-code is available for further study
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。
