III: Small: Building Linked Open Services from Online Sources

大多数关于语义网和更广泛的信息集成的工作都假定存在准确的源语义模型。在实践中，尽管Web上有大量可用的数据，但很少有对数据源和它们提供的信息的语义描述。该项目将开发一种新的方法，通过建立在语义Web中最近发展的关联开放数据的基础上，来解决自动发现和建模资源的问题。由此产生的系统将能够了解关联开放数据中存在背景知识的任何来源。这项工作将是对以前所做的工作的重大进步，因为它将允许智能系统扩展其知识，以学习覆盖系统没有已知来源的信息的来源模型。该系统将从链接开放数据中的所有数据和知识以及一些相关服务的语义描述（通过链接开放服务的工作）开始。然后，系统将通过利用关联开放数据中已经可用的知识来了解新资源与已知资源之间的关系。其结果将是可以在语义Web应用程序和信息集成系统中使用的单个源的丰富语义描述。自动发现和学习超越当前源描述的一系列源的详细语义描述的能力将极大地扩展语义Web和信息集成系统的实用性。这种能力将允许人们和系统更好地利用当今互联网上可用的大量数据，并提供一种工具来跟上其增长。例如，在生物信息学领域，数据量持续快速增长，发现和构建这些数据的能力将对研究人员充分利用所有这些信息来解决生物医学研究问题的能力产生重大影响，例如找到更有效的癌症治疗方法。有关该项目的更多信息可以在http://www.isi.edu/integration/people/knoblock/projects/prj_source_modeling.html上找到

Most of the work on the Semantic Web and more broadly Information Integration assumes that accurate semantic models of sources exist. In practice, although there is a tremendous amount of data available on the Web, there is rarely any semantic description of the sources and the information provided by them. This project will develop a new approach that addresses the problem of automatically discovering and modeling sources by building on the recent development of Linked Open Data within the Semantic Web. The resulting system will be able to learn about any source where there is background knowledge in the Linked Open Data. This work will be a significant advance over what was done previously since it will allow an intelligent system to expand its knowledge to learn models of sources that cover information for which the system has no known sources. The system will start with all of the data and knowledge in the Linked Open Data as well as semantic descriptions of some related services (through the work on Linked Open Services). The system will then learn how a new source relates to the known sources by exploiting the knowledge already available the the Linked Open Data. The result will be a rich semantic description of the individual sources that can be used in Semantic Web applications and information integration systems.The ability to automatically discover and learn detailed semantic descriptions across a range of sources that go beyond the current source descriptions will greatly expand the utility of Semantic Web and information integration systems. This capability will allow people and systems to better exploit the massive amount of data available today on the Internet and provide a tool to keep up with its growth. Within the bioinformatics world, for example, the amount of data continues to grow rapidly, and the ability to find and structure this data will have a significant impact on ability of researchers to fully exploit all of this information to solve biomedical research questions, such as finding more effective treatments for cancer. More information about the project can be found at http://www.isi.edu/integration/people/knoblock/projects/prj_source_modeling.html