Pattern mining from linked data, methods, tools and applications

这个程序是关于从发布在Web上的结构化数据中提取有用的知识。它研究了适应以前贡献的方法和技术的模式挖掘新兴的Web数据，云的链接开放数据（LOD）数据集在Web上免费访问/检索。底层格式是资源描述框架（RDF），一种基于图形的语言。云的LOD数据集通常具有显式的机器可读模式，它们相互链接成一个不断扩展的几乎唯一的全局图（2011年为320亿条边）。在这样一个巨大的异构但结构相对良好的数据源中进行数据挖掘是有益的，也是具有挑战性的：（1）LOD数据集的关系结构对于标准数据挖掘器来说很难处理，（2）需要适当地利用可用的模式知识来指导挖掘过程。我们的计划侧重于模式挖掘，即，发现数据描述中重复出现的错误。所提取的模式具有广泛的应用领域，从模式细化到推荐到决策。然而，现有的模式挖掘机失败的三种方式中的至少一种：扁平化的图到一个特征向量，从而失去了宝贵的信息挖掘，忽略了模式知识或表达效率低下。我们建议研究RDF数据集的可变模式语言的可扩展性，表达性和覆盖率之间的更好的权衡。这项研究将建立在挖掘方法的基础上，这些方法旨在处理丰富的模式（本体），尽管输入数据有所不同，而且规模较小。因此，关键的挑战是扩大其范围和效率。为此，我们将研究如何根据一般数据挖掘问题对其进行适当的重铸，以便能够应用最先进的数据结构、技术和结构结果。预期应用是药物不良反应分析（药物警戒）、重症监护决策、基因组数据系统发育分析中复杂工作流程的设计。

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.