Combining Semantic Technologies and Machine Learning for the (semi-) automatic annotation of data.

结合语义技术和机器学习来（半）自动注释数据。

• 批准号: 2248787
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2248787
• 关键词: Combining; Semantic; Technologies; Machine; Learning

Context:Identifying real-world entities and their structural relations in different data formats is of crucial importance for many applications including data integration, data cleaning, data mining, and knowledge discovery. In many domains, this is still a very labour-intensive task due to missing, incomplete or obfuscated metadata. The increase in the global data volume makes the manual annotation of data no longer feasible and requires a more scalable solution. New techniques for the (semi-) automatic data annotation would, therefore, add a lot of value in these domains. Goals/Objectives:The goal of the project is to investigate the (semi-) automatic annotation of data with meaningful metadata that describes the underlying structure and semantics. Preliminary research from the Information Systems group at the University of Oxford has shown that the combination of semantic technologies (such as large online knowledge graphs and ontologies) and state-of-the-art machine learning techniques (such as deep neural networks and semantic embedding) delivered very promising results for the annotation of relational data. This indicates great potential for further research regarding the annotation of more complex structural relationships on relational data as well as the annotation of other structured, semi-structured or unstructured data formats. Novelty of the research methodology:There exists only a limited number of publications that use a combination of semantic technologies (e.g. online knowledge graphs and ontologies) and deep learning (e.g. deep neural networks and semantic embedding) for the automatic annotation of data. Additionally, most of this work focuses on relational data. The combination of technologies and the application on different data formats constitute a novel research methodology unique to this project. Alignment to EPSRC's strategies and research areas (which EPSRC research area the project relates to):'This project falls within the EPSRC Information and Communication Technologies research area'It is especially related to topics such as:Information SystemsDatabasesMachine Learning/Artificial Intelligence Company collaborators:The Information Systems group in Oxford works in collaboration with the Siemens research group in Munich on this project. The Siemens team contributes realistic use-cases as well as considerable research expertise and resources in this area.

背景：识别不同数据格式的真实世界实体及其结构关系对于许多应用至关重要，包括数据集成，数据清洗，数据挖掘和知识发现。在许多领域，由于元数据缺失、不完整或模糊，这仍然是一项非常劳动密集型的任务。全球数据量的增加使得数据的手动注释不再可行，需要更可扩展的解决方案。因此，（半）自动数据注释的新技术将在这些领域中增加很多价值。目标：该项目的目标是研究（半）自动注释数据与有意义的元数据，描述了底层结构和语义。牛津大学信息系统小组的初步研究表明，语义技术（如大型在线知识图和本体）和最先进的机器学习技术（如深度神经网络和语义嵌入）的结合为关系数据的注释提供了非常有前途的结果。这表明进一步研究关系数据上更复杂的结构关系的注释以及其他结构化、半结构化或非结构化数据格式的注释具有很大的潜力。研究方法的新奇：只有有限数量的出版物使用语义技术（例如在线知识图和本体）和深度学习（例如深度神经网络和语义嵌入）的组合来自动注释数据。此外，这项工作的大部分重点是关系数据。技术的结合和不同数据格式的应用构成了该项目独特的新研究方法。 符合EPSRC的战略和研究领域（该项目涉及EPSRC的研究领域）：“该项目福尔斯属于EPSRC信息和通信技术研究领域”它特别涉及以下主题：信息系统数据库机器学习/人工智能公司合作者：牛津的信息系统小组与慕尼黑的西门子研究小组合作开展该项目。西门子团队在这一领域提供了现实的用例以及大量的研究专业知识和资源。