基于扩展激活技术的图结构节点分类的可扩展框架:ONTOSPREAD方法

J. Rodríguez, Jose Emilio Labra Gayo, P. O. Pablos
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引用次数: 8

摘要

本文提出了一种扩展激活技术的框架。该技术由ONTOSPREAD框架支持,该框架支持在基于图形的结构上开发、配置、自定义和执行Spreading Activation方法。在信息和文档检索领域,语义网络一直被用于高效地探索基于语义网络的知识库。新兴的数据Web和大量可用的信息使得基于现有词汇表和数据集的重用部署新的服务和应用程序成为可能。大量此类信息使用语义web语言和格式发布,如RDF,使用W3C标准语言开发的隐式图结构;但是需要新的灵活和可扩展的方法来创建增值服务和利用数据。这就是为什么ONTOSPREAD被认为是相关的,它提供了一种新的方法来实现基于图的结构中概念的激活和传播的双重过程,更具体地说,是在Web of Data领域中浏览和排序资源。最初的约束,如根据距离的权重退化,与来自该技术扩展的其他约束(如收敛路径奖励)相结合。最后给出了一种评估方法和两个使用著名本体GALEN和SNOMED CT的例子来验证该技术的优点、改进和能力。
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An Extensible Framework to Sort out Nodes in Graph-Based Structures Powered by the Spreading Activation Technique: The ONTOSPREAD Approach
This paper presents an extensible framework for the Spreading Activation technique. This technique is supported by the ONTOSPREAD framework enabling the development, configuration, customization, and execution of the Spreading Activation method on graph-based structures. It has been used for a long time to the efficient exploration of knowledge bases built on semantic networks in Information and Document Retrieval domains. The emerging Web of Data and the sheer mass of information now available make it possible the deployment of new services and applications based on the reuse of existing vocabularies and datasets. A large amount of this information is published using semantic web languages and formats such as RDF, implicit graph structures developed using W3C standard languages; but new flexible and scalable methods to create added-value services and exploit the data are required. That is why ONTOSPREAD is considered relevant in providing a new way to implement the double process of activation and spreading of concepts in graph-based structures, more specifically to browse and rank resources in the Web of Data realm. The original constraints like weight degradation according to the distance are provided in combination with others coming from the extension of this technique like the converging paths reward. Finally an evaluation methodology and two examples using the well-known ontologies GALEN and SNOMED CT are presented to validate the goodness, the improvement, and the capabilities of this technique.
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