基于模糊语言方法的信息检索系统的研究

该文提出了一个基于模糊语言方法的信息检索系统模型。该系统分为查询界面子系统、数据库子系统和检索子系统三大部分。在查询界面子系统,用布尔表达式表示用户的查询请求,并对每个查询关键词赋予了两种不同语义的语言值权重,该权重表达了用户的模糊检索要求;在数据库子系统,用索引词一文档模糊矩阵表示待检索的文档,对每个索引词。根据其在文档中的出现频率大小。引入了数值权重;在检索子系统,运用模糊语言方法,对用户输入的布尔查询表达式与索引词一文档模糊矩阵进行自底向上的模糊匹配,最后返回满足用户要求的检索结果。相对于传统的基于查询关键词精确匹配的检索系统而言,该系统能较好地满足用户查询要求中的灵活性。     

基于词重要性的Markov网络查询扩展模型

词项权重已经广泛应用于信息检索模型中,针对传统的词项独立性假设的词袋模型的问题,本文将基于词重要性的词项权重的计算方法应用于Markov网络查询扩展模型中。该词项权重的计算方法须先建立文档的词项图,然后根据词项图得到词项的共现矩阵和词项间的概率转移矩阵,最后利用Markov链的计算方法得到词的权重。将得到的词项权重代入Markov网络扩展模型中,在5个标准数据集上的实验结果表明,采用基于词重要性的Markov网络查询扩展模型的检索结果优于传统的基于词袋的检索结果。     

一种基于相关反馈的信息检索模型

针对现有信息检索系统难以按查询需求处理检索文档的问题,提出了一种基于相关反馈的信息检索模型,分析了查询词分解,推导了相关反馈机制和正规化过程,并进一步阐述了文档提取方法。提出的模型通过相关反馈和查询词扩展,克服了传统方法无法计算文档与查询词之间的相似度问题,并能有效地处理检索文档。仿真结果证明了该模型的有效性和可行性。     

基于多层Markov网络的信息检索模型

随着信息检索技术的不断发展,挖掘更加有效的信息来提高检索精度成为研究热点,已有的研究表明在检索过程中有效地融合各种信息将得到更好的检索效果。对一个具体查询而言,可以充分利用与已有查询的相关性、词语相关性和文档相关性等信息进行查询扩展和重构。基于这种思路,该文分别构造查询网络、词网络和文档网络,提出了多层Markov网络的信息检索模型,模型可以融合词间关系、文档间关系和查询间关系,为了有效降低计算量,给出了基于团计算模型。在标准数据集上的实验表明该文的模型能够有效融合三类信息,并较大幅度地提高检索效果。     

基于迭代方法的多层Markov网络信息检索模型

查询扩展是提高检索效果的有效方法,传统的查询扩展方法大都以单个查询词的相关性来扩展查询词,没有充分考虑词项之间、文档之间以及查询之间的相关性,使得扩展效果不佳。针对此问题,该文首先通过分别构造词项子空间和文档子空间的Markov网络,用于提取出最大词团和最大文档团,然后根据词团与文档团的映射关系将词团分为文档依赖和非文档依赖词团,并构建基于文档团依赖的Markov网络检索模型做初次检索,从返回的检索结果集合中构造出查询子空间的Markov网络,用于提取出最大查询团,最后,采用迭代的方法计算文档与查询的相关概率,并构建出最终的基于迭代方法的多层Markov网络信息检索模型。实验结果表明 该文的模型能较好地提高检索效果。     

模糊粗糙集在科技文档检索中的应用研究

关于提高文献的检索效率,在科技文献检索过程中,传统的基于关键词匹配的检索方法缺乏对知识的理解和处理,只能检索出包含关键词的文献,而不能检索出与关键词语义相似的文献,因而检索结果在查全率和查准率都无法满足检索者的要求.将模糊粗糙集理论引入信息检索当中,对信息检索模型的缺陷进行了改进.首先用传统的互信息函数计算标引词之间的语义关联权重,构建出模糊近似空间;然后用TF - IDF方法获得文档的模糊向量表示,在计算标引词重要度权重时,不但考虑了标引词出现的频度,还考虑位置因素,查询的模糊向量表示完全由用户的兴趣确定;最后用模糊近似空间对关键词进行概念扩展,挖掘出相似概念类,计算文档和查询模糊表示的上、下近似集,文档和查询的匹配不再是关键词匹配,而是利用布尔逻辑的合取、析取公式对上、下近似集进行模糊匹配,并返回按相似度值排序的检索结果.仿真测试表明,方法能提高科技文档检索的性能,能对科技文献进行概念意义上的检索.     

基于层次依赖的Markov网络信息检索扩展模型

查询扩展是解决查询词与相关文档中的词不匹配而导致检索效率低下问题的关键技术之一。提出了基于层次依赖的Markov网络信息检索扩展模型。该模型综合考虑了候选词与查询词的层次距离、词间相关性、词节点的出度和路径等因素,通过层次依赖关系对候选词进行重新加权,选择与查询最为相关的候选词应用于信息检索扩展模型,有利于挖掘出更多潜在的、深层次依赖关系的查询候选词。在5个标准数据集上进行了实验,结果表明基于层次依赖的Markov网络信息检索扩展模型与未进行查询扩展的BM25模型相比,在3-avg和11-avg上分别提高了5%~41%和5%~70%不等,与基于直接相关的Markov网络信息检索扩展模型相比,该模型在总体检索效率上表现更优。     

基于局部类别分析和遗传算法的查询优化算法

查询扩展是信息检索中优化查询的一种有效方法。针对信息检索中用户查询关键词与文档标引词不匹配的问题,提出一种基于局部类别分析和遗传算法的查询优化算法。该算法分两个阶段实现:第1阶段对用户提交的查询Qold进行扩展,采用基于局部类别分析的查询扩展方法选择查询扩展词构成新查询Qnew;第2阶段对新查询Qnew进行权重分配,采用遗传算法对扩展后的查询进行权重调整得到最优查询向量,再次对测试集中的文档进行二次检索。实验结果表明,该算法比单独使用局部上下文分析算法、局部类别分析算法均有更优的检索性能。     

基于查询扩展词条加权的文本检索研究

本文分析了关键词检索文本,由于其查询词没有扩展导致检全率低;而概念检索文本虽然部分有检索词扩展,但是查询词权重与原查询词没有区分.为此,本文利用词条间的语义相似度,提出一种查询扩展词条权重计算方法--展开减小法,并将查询词以及扩展词经展开减小法计算权重后构建向量空间模型检索文本.实验表明,构建的检索模型检索文本,其综合...     

基于文档实例的中文信息检索

传统的信息检索系统基于关键词建立索引并进行信息检索.这些系统存在查询返回文档集大、准确率低和普通用户不便于构造查询等不足.为此,该文提出基于文档实例的信息检索,即以已有文档作为样本,在文档库中检索与样本文档相似的所有文档.文中给出了基于文档实例的中文信息检索的解决方法和实现技术.初步实验结果表明该方法是行之有效的.     

Top-<Emphasis Type="Italic">k</Emphasis> coupled keyword recommendation for relational keyword queries

Providing top-k typical relevant keyword queries would benefit the users who cannot formulate appropriate queries to express their imprecise query intentions. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query suggestion approach which can provide typical and semantically related queries to the given query. Firstly, a keyword coupling relationship measure, which considers both intra- and inter-couplings between each pair of keywords, is proposed. Then, the semantic similarity of different keyword queries can be measured by using a semantic matrix, in which the coupling relationships between keywords in queries are reserved. Based on the query semantic similarities, we next propose an approximation algorithm to find the most typical queries from query history by using the probability density estimation method. Lastly, a threshold-based top-k query selection method is proposed to expeditiously evaluate the top-k typical relevant queries. We demonstrate that our keyword coupling relationship and query semantic similarity measures can capture the coupling relationships between keywords and semantic similarities between keyword queries accurately. The efficiency of query typicality analysis and top-k query selection algorithm is also demonstrated.     

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.     

No-but-semantic-match: computing semantically matched xml keyword search results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.     

Search for the Blind : A schema independent query method using concept hierarchy and keywords

Some database schema is very complex and it changes more frequently than the past. On the other hands, in such application database as scientific database and decision-support database, the importance of ad-hoc queries is expected to increase. Therefore the ability of a system to support ad-hoc queries without knowing the database schema information is needed. In this paper, we will describe a method to create candidate structured queries with concept hierarchy using user-input keywords. We will create structured queries in the following step; first, we extract the schema items related to keywords, and second, we compose possible paths between schema items, thus formulate candidate SQL queries.     

基于词语关联度的查询缩略

冗长查询指用户提交的句子成份复杂的查询。当前的搜索引擎对于关键字的检索取得了较好的结果。但是对于冗长的查询,如果将所有词作为关键字进行检索,往往只能返回相当有限的结果。我们尝试利用关键词之间的词语关联度,发现语义蕴含,删除“信息量”小的关键词,提高检索的效果。对于实验结果,我们分别从“面向机器”和“面向用户”两个角度进行评价。在“面向机器”的评价部分,我们根据搜索引擎返回结果的标红率和结果数进行自动评价;在“面向用户”的评价部分,我们对搜索结果文档进行人工评价。实验结果表明,我们的方法能够明显提高检索结果的数量和质量。     

Hermes: Data Web search on a pay-as-you-go integration infrastructure

The Web as a global information space is developing from a Web of documents to a Web of data. This development opens new ways for addressing complex information needs. Search is no longer limited to matching keywords against documents, but instead complex information needs can be expressed in a structured way, with precise answers as results. In this paper, we present Hermes, an infrastructure for data Web search that addresses a number of challenges involved in realizing search on the data Web. To provide an end-user oriented interface, we support expressive user information needs by translating keywords into structured queries. We integrate heterogeneous Web data sources with automatically computed mappings. Schema-level mappings are exploited in constructing structured queries against the integrated schema. These structured queries are decomposed into queries against the local Web data sources, which are then processed in a distributed way. Finally, heterogeneous result sets are combined using an algorithm called map join, making use of data-level mappings. In evaluation experiments with real life data sets from the data Web, we show the practicability and scalability of the Hermes infrastructure.     

Strategy for mining association rules for web pages based on formal concept analysis

To avoid returning irrelevant web pages for search engine results, technologies that match user queries to web pages have been widely developed. In this study, web pages for search engine results are classified as low-adjacence (each web page includes all query keywords) or high-adjacence (each web page includes some of the query keywords) sets. To match user queries with web pages using formal concept analysis (FCA), a concept lattice of the low-adjacence set is defined and the non-redundancy association rules defined by Zaki for the concept lattice are extended. OR- and AND-RULEs between non-query and query keywords are proposed and an algorithm and mining method for these rules are proposed for the concept lattice. The time complexity of the algorithm is polynomial. An example illustrates the basic steps of the algorithm. Experimental and real application results demonstrate that the algorithm is effective.     

Using document access sequences to recommend customized information

WordSieve, a text analysis algorithm, uses a competitive-network-learning approach to learn topic-relevant keywords in real time with no predetermined corpus. You can use these keywords to form search engine queries to suggest relevant documents to the user.     

Keyword proximity search in XML trees

Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches.     

Adaptive searching in succinctly encoded binary relations and tree-structured documents

The methods most heavily used by search engines to answer conjunctive queries on binary relations (such as one associating keywords with web-pages) are based on computing the intersection of postings lists stored as sorted arrays and using variants of binary search. We show that a succinct representation of the binary relation permits much better results, while using less space than traditional methods. We apply our results not only to conjunctive queries on binary relations, but also to queries on semi-structured documents such as XML documents or file-system indexes, using a variant of an adaptive algorithm used to solve conjunctive queries on binary relations.