超越文本的图形搜索:语义超链接数据中的关系搜索

M. Goldberg, J. Greenman, B. Gutting, M. Magdon-Ismail, J. Schwartz, W. Wallace
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引用次数: 1

摘要

我们提出了一种基于节点i度概念的语义图索引和搜索方案。i度允许在图上执行的搜索使用“类型”和连接信息,而不是文本标签来识别节点。我们的目标是在一个大型语义图(数据库)中识别一个网络图(片段)。片段可能表示研究人员在感兴趣的子网络上收集的不完整信息。虽然文本标签可能可用,但它们非常不可靠,不能用于识别隐藏网络。由于这个问题来自于识别同构子图的经典np困难问题,因此我们的算法是启发式的。
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Graph search beyond text: Relational searches in semantic hyperlinked data
We present novel indexing and searching schemes for semantic graphs based on the notion of the i.degrees of a node. The i.degrees allow searches performed on the graph to use “type” and connection information, rather than textual labels, to identify nodes. We aim to identify a network graph (fragment) within a large semantic graph (database). A fragment may represent incomplete information that a researcher has collected on a sub-network of interest. While textual labels might be available, they are highly unreliable, and cannot be used for identification of hidden networks. Since this problem comes from the classically NP-hard problem of identifying isomorphic subgraphs, our algorithms are heuristic.
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