Hybrid Intelligence for Knowledge Graph Construction

知识图谱构建的混合智能

• 批准号: 2887656
• 金额: --
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887656
• 关键词: Hybrid; Intelligence; Knowledge; Graph; Construction

Knowledge Graphs (KG) [11] have risen to be one the most powerful mechanismsto represent knowledge and have been shown to play an important role for manyapplications, including chatbot development, data integration, and semanticsearch [17].However, most data sources are generally still represented via heterogeneousnon-graph data structures, schemes, and formats [7]. The conversion of thesesources into KG views still necessitates a considerable effort from both KGengineers and domain experts when completing each of the tasks in the pipeline,proving this to be a time consuming and often complicated process [1].While advances have been made in terms of supporting KG engineers in theirwork, "existing techniques offer [...] solutions, each covering a specific aspect ofKG construction, but automatically orchestrating [...] the whole constructionprocess remains the challenge" [12]. This challenge motivates our research.Therefore, our objectives are (i) enriching the theoretical understanding ofKG construction in the context of data integration processes and automation,(ii) extending the application domain of AI to the orchestration of KG construction tasks into pipelines and (iii) supporting the community of practitioners bysupplying them with novel methods for KG construction pipeline design, generation and automation.We propose to undertake this by first researching and modelling KG construction pipelines to discover and represent general features and operations.Then, basing ourselves on the actions of the KG engineer understood as a goal oriented agent, we will propose the design of a hybrid intelligent problem-solvingagent as a possible solution to the automatic orchestration of these data integration pipelines, focusing on structured and semi-structured sources.

知识图（Knowledge Graphs， KG）已经成为表达知识的最强大的机制之一，并在许多应用中发挥着重要作用，包括聊天机器人开发、数据集成和语义搜索。然而，大多数数据源通常仍然通过异构的非图数据结构、模式和格式[7]来表示。在完成管道中的每个任务时，将这些资源转换为KG视图仍然需要KGengineers和领域专家付出相当大的努力，这证明这是一个耗时且通常复杂的过程[1]。虽然在支持KG工程师的工作方面取得了进展，但“现有技术提供[…]解决方案，每个解决方案涵盖kg构建的特定方面，但自动编排[…]整个建设过程仍然是一个挑战。这一挑战激发了我们的研究。因此，我们的目标是(i)在数据集成过程和自动化的背景下丰富对KG施工的理论理解，（ii）将人工智能的应用领域扩展到KG施工任务到管道的编排，以及（iii）通过为从业者提供KG施工管道设计，生成和自动化的新方法来支持他们。我们建议首先对KG施工管道进行研究和建模，以发现和表示其一般特征和操作。然后，将KG工程师的行为理解为一个目标导向的代理，我们将提出一个混合智能问题解决代理的设计，作为这些数据集成管道自动编排的可能解决方案，重点关注结构化和半结构化源。