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Effective top-k computation with term-proximity support

机译：有效的top-k计算，支持术语接近

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摘要

Modern web search engines are expected to return the top-k results efficiently. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignoring some especially important factors in ranking functions, such as term-proximity (the distance relationship between query terms in a document). In our recent work [Zhu, M., Shi, S., Li, M., & Wen, J. (2007). Effective top-fc computation in retrieving structured documents with term-proximity support. In Proceedings of 16th C1KM conference (pp. 771-780)]. we demonstrated that, when term-proximity is incorporated into ranking functions, most existing index structures and top-fc strategies become quite inefficient. To solve this problem, we built the inverted index based on web page structure and proposed the query processing strategies accordingly. The experimental results indicate that the proposed index structures and query processing strategies significantly improve the top-fc efficiency. In this paper, we study the possibility of adopting additional techniques to further improve top-fc computation efficiency. We propose a Proximity-Probe Heuristic to make our top-fc algorithms more efficient. We also test the efficiency of our approaches on various settings (linear or non-linear ranking functions, exact or approximate top-fc processing, etc.).
机译：现代网络搜索引擎有望有效返回前k个结果。尽管已经提出了许多动态索引修剪策略来进行有效的top-k计算，但是大多数策略都倾向于忽略排名函数中一些特别重要的因素，例如术语接近度（文档中查询词之间的距离关系）。在我们最近的工作中[Zhu，M.，Shi，S.，Li，M.，＆Wen，J.（2007）。有效的top-fc计算，可检索带有术语邻近支持的结构化文档。在第16届C1KM会议论文集（第771-780页）]。我们证明，将术语接近度合并到排名函数中后，大多数现有的索引结构和top-fc策略变得效率很低。为了解决这个问题，我们建立了基于网页结构的倒排索引，并提出了相应的查询处理策略。实验结果表明，所提出的索引结构和查询处理策略显着提高了top-fc效率。在本文中，我们研究了采用其他技术进一步提高top-fc计算效率的可能性。我们提出了一种Probeimity-Probe启发式算法，以使我们的top-fc算法更加高效。我们还测试了在各种设置（线性或非线性排名函数，精确或近似top-fc处理等）下我们方法的效率。

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