一种改进了的查询扩展算法

在进行检索的时候,用户给出的查询关键字常常不能准确地表达出用户的真实意图,所以要对用户的查询关键字进行扩展。该文章改进了基于本体的查询关键字扩展算法,加入了反映与用户意图符合程度的贴近度系数以及扩展短语对文本重要程度的隶属度系数。经实验证实,改进后的算法在查全率和查准率方面有明显的提高。     

keymanticES:数据空间中的语义关键字实体查询机制

由于数据空间自身的特点使得数据空间中的关键字查询与Web上和关系数据库上的关键字查询有着很大的差别,已有的关键字查询技术不能完全满足和适用数据空间环境.关键字查询的简略性和无结构性所带来的模糊语义,无法准确地理解用户的查询意图使得查询结果不能很好地满足用户需求等.本文提出一种数据空间中的语义关键字实体查询机制keymanticES,着重介绍了关键字查询意图消歧的方法从而较好地解决了关键字查询的语义模糊问题,提高了关键字查询的准确率.实验结果表明所提出方法的有效性和正确性.     

基于用户反馈的关系数据库关键字查询系统

关系数据库中的关键字查询问题使得用户无需掌握查询SQL语言与数据库模式相关知识就可以进行数据库查询,因此受到人们的广泛关注,许多方法和原型被提出.当前流行的关系数据库中关键字查询技术存在较慢的查询时间或者不准确的查询结果.针对这两个问题,提出了一种基于用户反馈的查询方法,根据用户输入的关键字动态地生成一个在数据库中包含关键字的属性所组成的Form表单提供给用户,用户在Form中进行选择并提交,最后,根据用户的选择和数据库模式图进行连接算法并执行SQL获得最终结果.     

基于日志挖掘的查询概念图扩展

提出将概念图引入查询扩展,从概念的层面上进行语义的扩展。使用概念图表示查询可以更准确地表明用户的查询意图,并在此基础上进行语义的扩展,通过这种方法给出的扩展查询更符合用户的查询意图。对用户查询进行基于概念图的查询扩展,并将结果与百度的相似查询进行了比较,证明基于概念图的查询扩展能更准确地把握用户的查询意图。     

基于关键字的RDF聚合查询研究

目前RDF数据上关键字查询转换为结构化语句的算法主要支持对于一般图元素的查询，而无法转换为包含聚合操作的结构化语句。关键字存在大量候选解释，且可能同时匹配聚合操作或图元素，这导致查询中聚合意图的理解非常困难。对此，提出将关键字查询自动转换为可能包含聚合操作的SPARQL语句的算法。算法对SPARQL所支持的聚合操作进行分类，获得关键字与聚合类别的匹配字典，进行关键字映射，计算关键字可能指示聚合意图的概率，确定候选查询解释，并利用模式图获得查询意图，设计意图分数计算方法和查询转换算法，得到对应的查询语句。LUBM和DBLP数据集上的实验验证了算法的有效性和准确性。     

Rtop-k:基于结构松弛的XML关键字近似查询方法

目前,现有的大多数关键字查询方法都是计算XML包含关键字元素的最紧致片段,这类方法大都忽略了XML文档中嵌入的结构关系,而XML结构化查询能够准确捕捉用户查询的信息,具有较高的准确率.将结构化查询方法与关键字信息检索相结合,通过分析关键字与XML文档的结构关系判断用户查询的需求,将面向对象的思想和松弛查询的方法引入到关键字查询方法中,提出一个新的XML关键字近似查询框架(Rtop-k).实验结果表明,所提近似查询方法能够较为准确地捕捉用户的查询意图,具有较高的查全率和查准率.     

Key-Tree：一种增强目录索引接口有限查询能力的方法

限于目录索引接口的查询能力．为了优化采用这种接口的信息查询系统．讨论了通用的基于目录索引的信息查询系统，提出一种优化算法通过对查询条件和查询结果进行分析、提取．构造出相关文档的关键字树．并基于关键字树对查询进行重写．生成由关键字组成的新的查询序列．使用生成的关键字序列重新搜索文档．比较两次查询结果并对其进行优先级排序．输出优化后的查询结果。实验结果证明本文提出的查询优化方法能够获得具有更高查全率(recall)和查准率(precision)的查询结果。     

一种结合自编码器与强化学习的查询推荐方法

查询推荐的目的是发掘搜索引擎用户的查询意图,并给出相关查询推荐.传统的查询推荐方法主要依靠人工提取查询的相关特征,如查询频率、查询时间、用户点击次数和停留时间等,并使用统计学习算法或排序算法给出查询推荐.近年来,深度学习方法在查询推荐问题上获得了广泛应用.现有的用于查询推荐的深度学习方法大多是基于循环神经网络,通过对查询日志中所有查询的语义特征进行建模以预测用户的下一查询.但是,现有的深度学习方法生成的查询推荐上下文感知能力较差,难以准确捕捉用户查询意图,且未充分考虑时间因素对查询推荐的影响,缺乏时效性和多样性.针对上述问题,文中提出了一种结合自编码器与强化学习的查询推荐模型(Latent Variable Hierarchical Recurrent Encoder-Decoder with Time Informa-tion of Query and Reinforcement Learning,VHREDT-RL).VHREDT-RL引入了强化学习联合训练生成器和判别器,从而增强了生成查询推荐的上下文感知能力;利用融合查询时间信息的隐变量分层递归自编码器作为生成器,使得生成查询推荐有更好的时效性和多样性.AOL数据集上的实验结果表明,文中提出的VHREDT-RL模型获得了优于基准方法的精度、鲁棒性和稳定性.     

XML数据流上基于关键字的多查询处理

试图将基于XML文档的关键字查询技术引入数据流环境中,在同时处理大量基于关键字的查询的基础上为用户返回有意义的数据片段.提出了一种基于有向无环图的索引来高效组织大量基于关键字的查询,用以降低查询匹配的代价;针对数据流的特点,提出了一种基于栈的临时结果缓存方法,用于过滤大量查询无关的数据节点;通过实验从不同角度对提出的算法的各项性能指标进行了实验验证.     

XML 数据流上Top-K 关键字查询处理

利用关键字可以在模式未知的情况下对XML数据进行查询.在当前的XML数据流上的关键字查询处理中,打分函数往往不能都满足各种用户不同的需求.提出了一种基于skyline的XML数据流上的Top-K关键字查询.对于这种查询,不需要考虑影响结果与查询相关性的复杂因素,只需利用skyline挑选与查询最相关的结果.提出了两种XML数据流上的有效的基于skyline的Top-K关键查询处理算法,包括对单查询和多查询的处理算法.通过扩展实验对两种算法的有效性和可扩展性进行了验证.经过实验验证,所提出的查询处理算法的效率几乎不受关键字个数、查询结果数量、查询数量等参数的影响,运行时间和文档大小大致呈线性关系.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

模糊XML关键字近似查询方法研究

在精确XML文档上的关键字查询方法的研究大多是基于LCA语义或者其变种语义(SLCA,ELCA等)开展的,将包含所有关键字的最紧致XML子树片段作为查询结果返回。但是这些基于LCA语义产生的查询结果中通常包含了大量的冗余信息,现实世界中存在着大量的不确定和模糊信息,因而如何从模糊XML文档中搜索到高质量的关键字查询结果是一个需要研究的问题。针对模糊XML文档上的关键字近似查询方法进行研究,通过引入最小连接树(MCT)的概念,提出在模糊XML文档上关键字查询的所有GDMCTs问题,并给出解决这一问题的基于栈的算法All fuzzy GDMCTs,该算法可以得到满足用户指定的子树大小阈值和可能性阈值条件的所有GDMCTs结果。实验表明,该算法在模糊XML文档上能够得到较高质量的关键字查询结果。     

Keyword query with structure: towards semantic scoring of XML search results

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. Scoring of XML search results is an important issue in XML keyword search. Traditional “bag-of-words” model cannot differentiate the roles of keywords as well as the relationship between keywords, thus is not proper for XML keyword queries. In this paper, we present a new scoring method based on a novel query model, called keyword query with structure (QWS), which is specially designed for XML keyword query. The method is based on a totally new view taken by the QWS model on a keyword query that, a keyword query is a composition of several query units, each representing a query condition. We believe that this method captures the semantic relevance of the search results. The paper first introduces an algorithm reformulating a keyword query to a QWS. Then, a scoring method is presented which measures the relevance of search results according to how many and how well the query conditions are matched. The scoring method is also extended to clusters of search results. Experimental results verify the effectiveness of our methods.     

XML 关键词检索的查询理解

与纯文本文档集相比,使用语义标签标注的半结构化的XML文档集,有助于信息检索系统更好地理解待检索文档.同样,结构化查询,比如SQL,XQuery和Xpath,相对于纯关键词查询更加清晰地表达了用户的查询意图.这二者都能够帮助信息检索系统获得更好的检索精度.但关键词查询因其简单和易用性,仍被广泛使用.提出了XNodeRelation算法,以自动推断关键词查询的结构化信息(条件/目标节点类型).与已有的推断算法相比,综合了XML文档集的模式和统计信息以及查询关键词出现的上下文及其关联关系等推断用户的查询意图.大量的实验验证了该算法的有效性.     

一种基于节点语义相关性的XML关键字查询算法

现有的XML关键字查询算法,通常只考虑节点间的结构信息,以包含关键字匹配节点的子树作为查询的结果,而节点间的语义相关性一直没有被充分利用。这也是导致现有查询算法的结果中普遍含有大量语义无关的冗余信息的主要原因。在该文中,我们首先对查询关键字的环境语义及节点间的语义相关性进行了定义,在此基础上,提出了一种新的关键字查询算法,寻找语义相关单元作为关键字查询的结果。这样获得的查询结果,一方面不含语义无关的冗余信息,另一方面也与用户的查询意图更加匹配。实验表明,该文提出的算法在查询效率和精确性上都有较大改进。     

A syntactic approach to twig-query matching on XML streams

Query matching on XML streams is challenging work for querying efficiency when the amount of queried stream data is huge and the data can be streamed in continuously. In this paper, the method Syntactic Twig-Query Matching (STQM) is proposed to process queries on an XML stream and return the query results continuously and immediately. STQM matches twig queries on the XML stream in a syntactic manner by using a lexical analyzer and a parser, both of which are built from our lexical-rules and grammar-rules generators according to the user's queries and document schema, respectively. For query matching, the lexical analyzer scans the incoming XML stream and the parser recognizes XML structures for retrieving every twig-query result from the XML stream. Moreover, STQM obtains query results without a post-phase for excluding false positives, which are common in many streaming query methods. Through the experimental results, we found that STQM matches the twig query efficiently and also has good scalability both in the queried data size and the branch degree of the twig query. The proposed method takes less execution time than that of a sequence-based approach, which is widely accepted as a proper solution to the XML stream query.     

A novel XML keyword query approach using entity subtree

Keyword query is an important means to find object information in XML document. Most of the existing keyword query approaches adopt the subtrees rooted at the smallest lowest common ancestors of the keyword matching nodes as the basic result units. The structural relationships among XML nodes are excessively emphasized but the semantic relevance is not fully exploited.To change this situation, we propose the concept of entity subtree and emphasis the semantic relevance among different nodes as querying information from XML. In our approach, keyword query cases are improved to a new keyword-based query language, Grouping and Categorization Keyword Expression (GCKE) and the core query algorithm, finding entity subtrees (FEST) is proposed to return high quality results by fully using the keyword semantic meanings exposed by GCKE. We demonstrate the effectiveness and the efficiency of our approach through extensive experiments.     

Adaptive relaxation for querying heterogeneous XML data sources

Searching XML data with a structured XML query can improve the precision of results compared with a keyword search. However, the structural heterogeneity of the large number of XML data sources makes it difficult to answer the structured query exactly. As such, query relaxation is necessary. Previous work on XML query relaxation poses the problem of unnecessary computation of a big number of unqualified relaxed queries. To address this issue, we propose an adaptive relaxation approach which relaxes a query against different data sources differently based on their conformed schemas. In this paper, we present a set of techniques that supports this approach, which includes schema-aware relaxation rules for relaxing a query adaptively, a weighted model for ranking relaxed queries, and algorithms for adaptive relaxation of a query and top-k query processing. We discuss results from a comprehensive set of experiments that show the effectiveness and the efficiency of our approach.     

一种基于区间预留编码的XML关键字查询算法

近年来,随着XML数据的爆炸式增长,对XML关键字查询技术的研究日益受到关注。数据编码是关键字查询的基础,目前主要有2种方式--基于路径的编码及区间编码。区间编码可更好地适应对查询中的XML数据进行动态的更新,因而具有更多的优势。本文研究基于区间编码的关键字查询问题,提出一种新的查询算法。该算法首先根据预留的区间值建立索引,再根据最小范围值对索引进行选择遍历,减少了不必要的比较,达到了提高查询效率的目的。研究发现,预留空间的选择对查询效率有一定的影响。为此,本文设计一种基于节点自身进行区间预留的编码方式（Interval Reservation Based on Node, IRBN）,为节点设置权值,并根据权值进行区间值的设定,形成根据节点自身分配区间的较为均衡的编码。实验表明,IRBN编码是合理的,有较高的查询效率。     

Accomplishing Deterministic XML Query Optimization

As the popularity of XML (extensible Markup Language) keeps growing rapidly, the management of XML compliant structured-document databases has become a very interesting and compelling research area. Query optimization for XML structured-documents stands out as one of the most challenging research issues in this area because of the much enlarged optimization (search) space, which is a consequence of the intrinsic complexity of the underlying data model of XML data. We therefore propose to apply deterministic transformations on query expressions to most aggressively prune the search space and fast achieve a sufficiently improved alternative (if not the optimal) for each incoming query expression. This idea is not just exciting but practically attainable. This paper first provides an overview of our optimization strategy, and then focuses on the key implementation issues of our rule-based transformation system for XML query optimization in a database environment. The performance results we obtained from experimentation show that our approach is a valid and effective one.