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

链接开放数据（LOD）正在迅速发展成为一种开放数据运动，使用万维网联盟（W3C）采用的标准连接万维网上的大量数据。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经增长到超过250亿个RDF三元组，并呈现出指数级的增长。然而，简单地将数据集合放在Web上的价值将非常有限。开发更强大的搜索、浏览、探索和分析的关键在于丰富LOD组件的互连或语义集成。考虑到规模、增长率、异质性和覆盖范围的增长，手动的语义集成或互连是不实际的。此外，目前的技术侧重于“相同”的关系，由于有限的表现力而被滥用。这就需要有方法来表示和识别不同实体之间更丰富、更明确的关系，以反映现实世界中存在的关系的丰富性。该项目开发了探索性技术，以丰富地互连LOD组件，然后解决查询LOD云的挑战，即获取需要访问、检索和组合来自LOD云的不同部分的信息的问题的答案。克服语义异构的技术包括：通过Wikipedia自举实现语义丰富；通过上层本体的抽象实现语义集成；以及可处理本体推理的大规模并行方法。具体而言，本研究将：(1)在实例级和模式级确定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；(3)通过本体推理获得隐含知识。由于该项目在新领域中涉足新路径，因此涉及重大风险，主要原因是缺乏关于高度自治数据源提供的数据的描述性信息（模式），源自独立数据源的数据之间存在显著的语法和语义异构性，规模明显更大，以及与快速变化和扩展的环境相关的不可预见的障碍。该项目旨在推动大量异构和自主开发或管理数据的语义集成。它试图从根本上改变LOD使用情况，因为成功的LOD查询是各种应用程序的关键推动因素。这个项目的成果可以为语义Web的开发和广泛采用奠定基础。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可以在http://knoesis.org/research/semweb/projects/ESQuILO上找到。

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.