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Efficient processing of keyword queries over graph databases for finding effective answers

机译：通过图形数据库有效处理关键字查询，以找到有效答案

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摘要

In this paper, we study on effective and efficient processing of keyword-based queries over graph databases. To produce more relevant answers to a query than the previous approaches, we suggest a new answer tree structure which has no constraint on the number of keyword nodes chosen for each keyword in the query. For efficient search of answer trees on the large graph databases, we design an inverted list index to pre-compute and store connectivity and relevance information of nodes to keyword terms in the graph. We propose a query processing algorithm which aggregates from the pre-constructed inverted lists the best keyword nodes and root nodes to find top-k answer trees most relevant to the given query. We also enhance the method by extending the structure of the inverted list and adopting a relevance lookup table, which enables more accurate estimation of the relevance scores of candidate root nodes and efficient search of top-fc answer trees. Performance evaluation by experiments with real graph datasets shows that the proposed method can find more effective top-fc answers than the previous approaches and provides acceptable and scalable execution performance for various types of keyword queries on large graph databases.
机译：在本文中，我们研究了图数据库上基于关键字的查询的有效处理。为了产生比以前的方法更相关的查询答案，我们建议一种新的答案树结构，该结构对查询中为每个关键字选择的关键字节点的数量没有限制。为了在大型图数据库上高效地搜索答案树，我们设计了一个倒排列表索引来预先计算并存储图中节点与关键字词的连通性和相关性信息。我们提出一种查询处理算法，该算法从预先构造的倒排列表中聚合最佳关键字节点和根节点，以找到与给定查询最相关的前k个答案树。我们还通过扩展倒排列表的结构并采用相关性查找表来增强该方法，从而可以更准确地估计候选根节点的相关性分数并有效地搜索top-fc答案树。通过对真实图形数据集进行实验的性能评估表明，与以前的方法相比，该方法可以找到更有效的top-fc答案，并且可以为大型图形数据库上的各种类型的关键字查询提供可接受的可扩展执行性能。

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