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factforge:数据服务和推断的知识的价值

FactForge: Data Service and the Value of Inferred Knowledge
课程网址: http://videolectures.net/dataforum2012_damova_factforge/  
主讲教师: Mariana Damova
开课单位: 锡玛集团控股公司
开课时间: 2012-07-16
课程语种: 英语
中文简介:
Linked Open Data运动正在走向成熟。不仅LOD云每年增加数十亿三倍,而且还开发了关于如何快速生成LOD,如何确保其质量以及如何提供垂直导向数据服务的技术和指南(LOD2,LATC,baseKB)。然而,关于如何在LOD框架中包括推理以及如何应对其多样性的问题很少。在本次演讲中,我们将介绍FactForge,这是一个关于数据网络的合理视图,它包含一段LOD云,例如, DBPedia,Freebase,Geonames,Wordnet,NY Times,Musicbrainz,Lingvoj,Lexvo,CIAFactbook,加载到单个存储库(OWLIM)中,并形成一个复合数据集,在其上执行推理。这使得查询可用知识增加了40%,达到约150亿个语句。 LOD的多样性使得它们的使用和查询极具挑战性,因为必须非常熟悉每个数据集下的模式。 schema.org,UMBEL,BLOOMS +,ALOCUS等倡议和研究项目试图在模式层面涉及黄金标准的概念,以便更好地实现LOD和WWW的互操作性,这表明了沿着这些方向寻找解决方案。 FactForge的新版本将在本次演讲中展示并在制作中持续数年,与这些观点保持一致。它提供了一个上层本体PROTON的参考层,它映射到FactForge中LOD数据集的本体,使它们的实例可以通过PROTON概念和属性访问。该参考层使得不需要加载LOD本体,优化推理过程,并允许新数据集与FactForge的整个LOD段快速无缝地数据集成。它还确保通过SPARQL更好地与其他组件连接,因为查询更紧凑,易于制定,响应时间更短,因为使用的连接更少,并且数据集中有大量推断知识,这使得知识发现真正的旅程,以及从不同立场的导航。 FactForge是执行推理的最大的一般知识体系和LOD。我们将展示利用FactForge的应用程序,并强调推理知识在其中产生的推理知识的作用,并将争论一种新的数据服务范例,不仅基于链接数据垂直,还基于推断知识。
课程简介: Linked Open Data movement is maturing. Not only LOD cloud increases by billions of triples yearly, but also technologies and guidelines about how to produce LOD fast, how to assure their quality, and how to provide vertical oriented data services are being developed (LOD2, LATC, baseKB). Little is said however about how to include reasoning in the LOD framework, and about how to cope with its diversity. In this talk we will present FactForge, a reason-able view on the web of data, which comprise a segment of LOD cloud, e.g. DBPedia, Freebase, Geonames, Wordnet, NY Times, Musicbrainz, Lingvoj, Lexvo, CIAFactbook, loaded in a single repository (OWLIM), and forming a compound dataset, on which inference is performed. This results in 40% increase of the knowledge available for querying to about 15 billion statements. The diversity of LOD makes their use and querying extremely challenging, as one has to be intimately familiar with the schemata underlying each dataset. Initiatives and research projects like schema.org, UMBEL, BLOOMS+, ALOCUS which try to involve the notion of a golden standard at schema level to allow better interoperability of LOD and the WWW in general, are indicative for the search of a solution along these lines. The new version of FactForge which will be shown in this talk and in the making for several years now, aligns with these views. It is supplied with a reference layer of the upper-level ontology PROTON, which is mapped to the ontologies of the LOD datasets in FactForge, making their instances accessible via PROTON concepts and properties. This reference layer makes loading of the LOD ontologies unnecessary, optimizing the reasoning processes, and allows for quick and seamless data integration of new datasets with the entire LOD segment of FactForge. It also ensures better interfacing with other components via SPARQL as the queries are more compact and easy to formulate, faster response times, because of less joins are employed, and a wealth of inferred knowledge across the datasets, which allows for real journey of knowledge discovery, and navigation from different stand points. FactForge is the largest body of general knowledge and LOD on which inference is performed. We will present applications which make use of FactForge and emphasize the role of inferred knowledge in them produced by the reason-able views, and will argue for a new paradigm of data services, based not only on linked data verticals but also on inferred knowledge.
关 键 词: 链接开放数据运动; LOD框架推理; 推断知识
课程来源: 视频讲座网
最后编审: 2020-06-11:dingaq
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