Pattern mining from linked data, methods, tools and applications

从关联数据、方法、工具和应用程序中进行模式挖掘

• 批准号: 249900-2013
• 负责人: Valtchev, Petko
• 金额: $ 1.46万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594358
• 关键词: Pattern; mining; linked; data; methods

This program is about extracting useful knowledge from the structured data that is published on the Web. It researches the adaptation of previously contributed methods and techniques for pattern mining to the emerging Web of Data, a cloud of Linked Open Data (LOD) datasets freely available on the Web for access/retrieval. The underlying format is the Resource Description Framework (RDF), a graph-based language. LOD datasets of the cloud, often with an explicit machine readable schema, are inter-linked into a virtually unique global graph which is constantly expanding (32 billion edges in 2011). Data mining within such a huge heterogeneous yet relatively well structured source is both beneficial and challenging: (1) the relational structure of a LOD dataset is hard to process for standard data miners and (2) the available schema knowledge need to be properly exploited to guide the mining process. Our program focuses on pattern mining, i.e., discovery of recurring regularities in data descriptions. The extracted patterns have a wide range of applications from schema refinement to recommendation to decision making in the domain. However, existing pattern miners fail in at least one of the three ways: flatten the graph into a feature vector and hence lose precious information for mining, ignore the schema knowledge or sacrify efficiency for expressiveness. We propose to research on a better trade-off between scalability, expressiveness and coverage of variable pattern languages for RDF datasets. The research will build upon mining methods designed to work with rich schemas (ontologies) albeit with a somewhat different input data and of smaller size. The key challenge is therefore to increase their scope and efficiency. To that end, we shall research on properly recasting them in terms of general data mining problems to enable the application of state-of-the-art data structures, techniques and structural results. The intended applications are analysis of drug adverse reactions (pharmacovigilance), decision making in critical care, design of complex workflows in phylogenetic analysis of genomic data.

这个程序是关于从Web上发布的结构化数据中提取有用的知识。它研究了模式挖掘方法和技术的适应性，以适应新兴的数据网络，这是一个在网络上免费访问/检索的链接开放数据（LOD）数据集的云。底层格式是资源描述框架（RDF），这是一种基于图的语言。云的LOD数据集通常具有明确的机器可读模式，它们相互连接成一个几乎唯一的不断扩展的全球图（2011年有320亿个边）。在如此庞大的异构但结构相对良好的数据源中进行数据挖掘既有益又具有挑战性：(1)标准数据挖掘者很难处理LOD数据集的关系结构；(2)需要适当利用可用的模式知识来指导挖掘过程。我们的项目侧重于模式挖掘，即发现数据描述中重复出现的规律。提取的模式具有广泛的应用，从模式细化到推荐再到领域中的决策制定。然而，现有的模式挖掘器在以下三种方式中至少有一种是失败的：将图扁平化为特征向量，从而失去宝贵的挖掘信息；忽略模式知识或牺牲表达效率。我们建议对RDF数据集的可变模式语言的可伸缩性、表达性和覆盖范围进行更好的权衡研究。该研究将建立在挖掘方法的基础上，这些挖掘方法旨在处理丰富的模式（本体），尽管使用的是不同的输入数据和较小的规模。因此，关键的挑战是增加它们的范围和效率。为此，我们将研究如何根据一般数据挖掘问题对它们进行适当的重铸，以便能够应用最先进的数据结构、技术和结构结果。预期的应用是药物不良反应的分析（药物警戒），重症监护中的决策制定，基因组数据系统发育分析中复杂工作流程的设计。