RDF Data Management

RDF数据管理

• 批准号: RGPIN-2014-03659
• 负责人: Özsu, MTamer
• 金额: $ 3.93万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587338
• 关键词: RDF; Data; Management

Graph data are of growing importance in many applications including the semantic web, social network analysis, bioinformatics, and physical communication networks. Graphs naturally model complicated structures in these fields, such as the relationships among people in a social network or the protein-protein interaction networks. The size and complexity of these graph data raise significant data management and data analysis challenges. My broad research scope is the study of these problems. In this discovery grant, my focus is on the graph structures that arise from models of Web resources. The Resource Description Framework (RDF) is the standard (proposed by W3C) by which Web objects are commonly modeled. RDF is a self-descriptive data model that is suitable for machine understanding and interpretation, and, therefore, expected to facilitate the “semantic web”. W3C has also defined a query language, called SPARQL, for accessing RDF repositories. RDF data sets have started to proliferate and grow. For example, Yago and DBPedia extract facts from Wikipedia and store them in RDF format to facilitate structural queries over Wikipedia; many local governments are now encoding the resources they provide to citizens in RDF format as part of the e-government initiatives; biologists have built elaborate RDF data collections (BioRDF and Uniprot RDF) for community sharing of experimental data; and Linked Open Data (LOD) initiative has been growing (as of September 2011 - which are the latest available information - over 31 million triples [tuples]) as a web data integration platform. Consequently, managing and analyzing large and distributed RDF datasets have emerged as an urgent and important concern. My group’s approach to RDF data management and analysis differs from many of the existing approaches that map, in one way or another, RDF into a relational representation and convert SPARQL queries into SQL. Although this has the advantage of leveraging mature technology, it gives rise to performance and modeling mismatch problems. We model an RDF dataset as a graph (which is the native model for RDF) and also represent a SPARQL query as a graph. Consequently, query execution reduces to graph matching. This approach has modeling and performance advantages. Within this general approach, I intend to study the following issues over the next five years: 1. Efficient storage structures for RDF graphs. 2. Efficient and effective query processing and optimization techniques for SPARQL queries (including aggregation queries that are now part of the SPARQL standard). 3. Distribution of RDF graphs and evaluation of SPARQL queries over distributed RDF stores. 4. Web data querying and integration using RDF, which requires some reasoning capability over RDF data (so called OWL 2 entailment regime). The methodology that will be followed includes algorithmic studies, development of prototype systems, and extensive experimentation. Successful completion of this research will result in the development of efficient and effective techniques for RDF data management, and web data integration and querying through RDF. Since RDF technology is now widely deployed (including by various levels of government at a number of countries), the results will have significant impact both technically and societally.

图形数据在许多应用中越来越重要，包括语义Web、社交网络分析、生物信息学和物理通信网络。图自然地模拟了这些领域中的复杂结构，例如社交网络中的人与人之间的关系或蛋白质相互作用网络。这些图形数据的大小和复杂性提出了重大的数据管理和数据分析挑战。我广泛的研究范围就是对这些问题的研究。 在这个发现基金中，我的重点是从Web资源模型中产生的图结构。资源描述框架（RDF）是Web对象通常建模的标准（由W3C提出）。RDF是一种自我描述的数据模型，适合于机器理解和解释，因此有望促进“语义网”。W3C还定义了一种名为SPARQL的查询语言，用于访问RDF存储库。RDF数据集已开始激增和增长。例如，Yago和DBPedia从Wikipedia中提取事实，并将其存储为RDF格式，以方便在Wikipedia上进行结构化查询;许多地方政府现在正在将其提供给公民的资源编码为RDF格式，作为电子政务计划的一部分;生物学家已经建立了精心制作的RDF数据集（BioRDF和Uniprot RDF）用于社区共享实验数据;和关联开放数据（LOD）计划一直在增长（截至2011年9月-这是最新的可用信息-超过3100万个三元组[元组]）作为Web数据集成平台。因此，管理和分析大型分布式RDF数据集已成为一个紧迫而重要的问题。 我的小组的RDF数据管理和分析方法与现有的许多方法不同，这些方法以某种方式将RDF映射为关系表示，并将SPARQL查询转换为SQL。虽然这具有利用成熟技术的优势，但它会引起性能和建模不匹配的问题。我们将RDF数据集建模为图（这是RDF的原生模型），并将SPARQL查询表示为图。因此，查询执行简化为图匹配。这种方法具有建模和性能优势。在这一总体方针下，我打算在今后五年研究以下问题： 1. RDF图的高效存储结构。 2. SPARQL查询的高效查询处理和优化技术（包括现在成为SPARQL标准一部分的聚合查询）。 3. RDF图的分布和分布式RDF存储上SPARQL查询的评估。 4. 使用RDF进行Web数据查询和集成，这需要对RDF数据进行一定的推理（所谓的OWL 2蕴涵机制）。 将遵循的方法包括算法研究，原型系统的开发和广泛的实验。 本研究的成功完成将导致开发高效和有效的RDF数据管理技术，以及通过RDF进行Web数据集成和查询。由于RDF技术现已广泛部署（包括一些国家的各级政府），其结果将在技术和社会方面产生重大影响。