Discovering Neighborhood Pattern Queries by sample answers in knowledge base
Discovering Neighborhood Pattern Queries by sample answers in knowledge base
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DOI：
10.1109/icde.2016.7498309
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Jialong Han;Kai Zheng;Aixin Sun;Shuo Shang;Ji-Rong Wen
中科院分区：
文献类型：
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作者：
Jialong Han;Kai Zheng;Aixin Sun;Shuo Shang;Ji-Rong Wen
知识库在促进网络搜索和问答等服务方面显示了其有效性。然而，它仍然是具有挑战性的普通用户完全理解知识库的结构，并发出结构化查询。在许多情况下，用户可能有一个自然语言问题，也知道一些流行的（但不是所有的）实体作为样本答案。在本文中，我们研究了反向top-k邻域模式查询问题，目的是发现结构查询的问题的基础上：（i）的知识库的结构，（ii）的样本答案的问题。所提出的解决方案包含两个阶段：过滤和细化。在过滤阶段，系统地探索候选查询的搜索空间。过滤掉结果集没有完全覆盖样本答案的无效查询。在细化阶段，验证所有幸存的查询，以确保它们与样本答案足够相关，假设样本答案比相关查询结果中的其他实体更知名或更受欢迎。提出了几种优化技术来加速精化阶段。为了进行评估，我们使用DBpedia知识库和一组现实生活中的问题进行了广泛的实验。实验结果表明，我们的算法是能够提供一个小的可能的查询，其中包含的查询匹配用户的问题在自然语言。
Knowledge bases have shown their effectiveness in facilitating services like Web search and question-answering. Nevertheless, it remains challenging for ordinary users to fully understand the structure of a knowledge base and to issue structural queries. In many cases, users may have a natural language question and also know some popular (but not all) entities as sample answers. In this paper, we study the Reverse top-k Neighborhood Pattern Query problem, with the aim of discovering structural queries of the question based on: (i) the structure of the knowledge base, and (ii) the sample answers of the question. The proposed solution contains two phases: filter and refine. In the filter phase, a search space of candidate queries is systematically explored. The invalid queries whose result sets do not fully cover the sample answers are filtered out. In the refine phase, all surviving queries are verified to ensure that they are sufficiently relevant to the sample answers, with the assumption that the sample answers are more well-known or popular than other entities in the results of relevant queries. Several optimization techniques are proposed to accelerate the refine phrase. For evaluation, we conduct extensive experiments using the DBpedia knowledge base and a set of real-life questions. Empirical results show that our algorithm is able to provide a small set of possible queries, which contains the query matching the user question in natural language.