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    李晓光 宋宝燕. GPE:一种基于图模型的NFS有意义结果确定模型[J]. 计算机研究与发展, 2010, 47(1): 174-181.
    引用本文: 李晓光 宋宝燕. GPE:一种基于图模型的NFS有意义结果确定模型[J]. 计算机研究与发展, 2010, 47(1): 174-181.
    Li Xiaoguang and Song Baoyan. GPE: A Graph-Based Determination Model for Meaningful NFS Query Result[J]. Journal of Computer Research and Development, 2010, 47(1): 174-181.
    Citation: Li Xiaoguang and Song Baoyan. GPE: A Graph-Based Determination Model for Meaningful NFS Query Result[J]. Journal of Computer Research and Development, 2010, 47(1): 174-181.

    GPE:一种基于图模型的NFS有意义结果确定模型

    GPE: A Graph-Based Determination Model for Meaningful NFS Query Result

    • 摘要: XML非完全结构查询(NFS)允许用户利用部分XML结构信息,甚至仅仅是关键字来描述查询要求,是在缺乏完整的XML文档结构信息情况下的重要查询手段.针对图模型下的NFS有意义结果判断问题,在PE模型基础上提出一种基于图的有意义结果判断模型GPE,包括结果粒度、模式实体定义、等价模式定义和判断规则;针对标签歧义性和复杂的结构语义, GPE提出一种结合基于领域字典的语境受限的标签语义相似性和模式结构相似性的等价模式计算方法.通过在实际数据集和XML实验数据上的实验表明,GPE模型在查准率和查全率上均有较大提高.

       

      Abstract: Non-fully structured query (NFS) is an important query approach for the XML documents lacking in full structure information. NFS query faces the situations: the user doesn’t know fully the structural knowledge of an XML document, or a document doesn’t provide any structural information, or documents are heterogeneous. However, the user can describe his querying requirement by an NFS query containing a part of XML structural information, or some keywords only. The issue of meaningful results determination is critical to the quality of NFS query. Based on the PE model for XML data of tree model in the authors, previous work, a graph-based meaningful determination model of NFS query results for XML data of graph model, called GPE, is proposed. the GPE model mainly includes the result’s granularity, the definition of pattern and entity, the definition of equivalent pattern, and determination rules. For the ambiguous label and complicate structural semantic, an equivalent pattern in the GPE is evaluated by combining a domain-dictionary-based and context-constricted label similarity with a pattern structure similarity. Such equivalent pattern evaluation can improve greatly the precision of meaningful results determination. With the extensive experiments on both the real dataset and XML benchmark, the GPE outperforms the PE model on both the recall and the precision.

       

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