III: EAGER - Expressive Scalable Querying over Integrated Linked Open Data

链接开放数据(LOD)正在迅速发展成为使用万维网联盟(W3C)采用的标准在万维网上连接各种数据的开放数据运动。在研究人员、政府机构和公司的推动下，由此产生的数据网络已增长到250亿RDF的三倍，并呈现指数增长。然而，简单地将数据集合放在Web上的价值将非常有限。为开发更强大的搜索、浏览、探索和分析而释放价值的关键是丰富地链接或语义集成LOD的组件。考虑到覆盖范围的大小、增长率、异质性和不断扩大的领域，手动语义集成或链接是不切实际的。此外，目前的技术关注的是“同为”关系，由于表现力有限，这一关系被滥用了很多。这个项目开发了探索性的技术来丰富地链接LOD的组件，然后解决查询LOD云的挑战，即获得问题的答案，这些问题需要从LOD云的不同部分访问、检索和组合信息。克服语义异构性的技术包括：通过维基百科自举来丰富语义；通过上层本体进行抽象来进行语义集成；以及用于易于处理的本体推理的大规模并行方法。具体地说，本研究将：(1)在实例和模式级别识别LOD数据集之间更丰富、更广泛和更相关的关系(这些关系将促进更好的知识发现、查询和本体映射)；(2)通过上层本体实现LOD查询联合；以及(3)通过本体推理实现对隐含知识的访问。该项目在新领域开辟新道路时存在巨大风险，主要原因是缺乏关于高度自主的数据源提供的数据的描述性信息(模式)、来自独立数据源的数据在句法和语义上的显著异质性、规模大得多，以及与快速变化和扩大的环境相关的不可预见的障碍。该项目旨在提高大量自主开发或管理的异质数据的语义集成的技术水平。它寻求从根本上改变LOD使用的格局，因为成功的LOD查询是各种应用程序的关键推动因素。该项目的结果可能为语义网的开发和深远的采用奠定基础。该项目与研究生和本科生的教育和研究型高级培训相结合。有关该项目的更多信息，请访问：http://knoesis.org/research/semweb/projects/ESQuILO.

Linked Open Data (LOD) is rapidly developing into an open data movement to connect a large variety of data across the World Wide Web using standards adopted by the World Wide Web Consortium (W3C). Driven by researchers, government agencies and companies, the resulting Web of Data has grown to over 25 billion RDF triples and is showing exponential growth. However, simply putting collections of data on the Web will be of very limited value. The key to unlocking the value for developing more powerful search, browsing, exploration and analysis is to richly interlink or semantically integrate components of LOD. Given the size, growth rate, heterogeneity and growing areas of coverage, manual semantic integration or interlinking is not practical. Furthermore, current techniques focus on 'same-as' relationship, which is much abused due to limited expressivity. This calls for ways to represent and identify richer and more explicit relationships between different entities that reflect the richness of relations that exist in the real world.This project develops exploratory techniques to richly interlink components of LOD and then addresses the challenge of querying the LOD cloud, i.e., of obtaining answers to questions which require accessing, retrieving and combining information from different parts of the LOD cloud. Techniques for overcoming semantic heterogeneity include: semantic enrichment through Wikipedia bootstrapping; semantic integration through abstraction by means of upper-level ontologies; and, massively parallel methods for tractable ontology reasoning. Specifically, this research will: (1) identify richer, broader, and more relevant relationships between LOD datasets at instance and schema level (these relationships will promote better knowledge discovery, querying, and mapping of ontologies); (2) realize LOD query federation through an upper level ontology; and, (3) enable access to implicit knowledge through ontology reasoning. The project involves significant risk as it treads new paths in a new terrain, primarily due to the lack of descriptive information (schema) about the data provided by highly autonomous data sources, the significant syntactic and semantic heterogeneity among data originating from independent data sources, and the significantly larger scale, as well as unforeseeable obstacles associated with a rapidly changing and expanding environment. This project aims to advance the state of the art in semantic integration of large amounts of heterogeneous and autonomously developed or managed data. It seeks to fundamentally transform the landscape of LOD usage because successful LOD querying is a key enabler for a variety of applications. The results of this project could set the stage for the development, and the far reaching adoption, of Semantic Web. The project is integrated with education and research-based advanced training of graduate and undergraduate students. Additional information about the project can be found at: http://knoesis.org/research/semweb/projects/ESQuILO.