一种基于权重矩阵的临近词检索问题解决框架

传统的信息检索模型假设查询中的关键词之间是并列关系,但用户的需求往往应该被抽象为一系列的关键词组,组内的关键词间具有更为紧密的语义关系,这就是定义的临近词检索问题.提出了基于权重矩阵的临近词检索问题解决框架,该框架将文档和查询抽象化为文档的权重矩阵表示和查询权重矩阵,通过计算两个矩阵间的相似度来实现临近词检索.实验结果证明,针对临近词检索问题,传统的信息检索模型只是一种简化问题的解决方案,权重矩阵框架从理论上和形式上更加契合临近词检索问题,查准率得到了显著的提高.     

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.     

XML文档语义检索方法研究

由于半结构文档如XML越来越广泛的应用,在数据库和信息检索领域,对半结构XML数据相似度的研究也变得尤为重要。给定XML文档集D和用户查询q,XML检索即是从D中查找出符合q的XML文档。为了有效地进行XML信息检索,提出了一种新的计算用户查询与XML文档之间相似度的算法。该算法分为三步：基于WordNet对用户查询q进行同义词扩展得到q';将q'和D中的每一篇XML文档都进行数字签名,并通过签名之间的匹配对D进行有效过滤,除去大量不符合用户查询的文档,得到一个文档子集D',[D'?D];对q'与D'中的文档进行精确匹配得到检索结果。     

云计算下支持语义的可搜索加密方法研究

传统的云计算下的可搜索加密算法没有对查询关键词进行语义扩展,导致了用户查询意图与返回结果存在语义偏差,并且对检索结果的相关度排序不够合理,无法满足用户对智能搜索的需求。对此,提出了一种支持语义的可搜索加密方法。该方法利用本体知识库实现了用户查询的语义拓展,并通过语义相似度来控制扩展词的个数,防止因拓展词过多影响检索的精确度。同时,该方法利用文档向量、查询向量分块技术构造出对应的标记向量,以过滤无关文档,并在查询-文档的相似度得分中引入了语义相似度、关键词位置加权评分及关键词-文档相关度等影响因子,实现了检索结果的有效排序。实验结果表明,该方法在提高检索效率的基础上显著改善了检索结果的排序效果,提高了用户满意度。     

基于位置语言模型的中文信息检索系统的研究

在大多数现有的检索模型中常常忽略了如下事实:一个文档中匹配到的查询词项的近邻性和打分时所基于的段落检索也可以被用来促进文档的打分。受此启发,提出了基于位置语言模型的中文信息检索系统,首先通过定义位置传播数的概念,为每个位置单独地建立语言模型;然后通过引入KL-divergence检索模型,并结合位置语言模型给每个位置单独打分;最后由多参数打分策略得到文档的最终得分。实验中还重点比较了基于词表和基于二元两种中文索引方法在位置语言模型中的检索效果。在标准NTCIR5、NTCIR6测试集上的实验结果表明,该检索方法在两种索引方式上都显著改善了中文检索系统的性能,并且优于向量空间模型、BM25概率模型、统计语言模型。     

图结构模糊XML文档上的模式匹配算法

模糊XML文档是指包含不确定信息的XML文档。在模糊XML文档查询方面,现有的研究成果较少,并且都是基于树型结构的XML文档进行的。针对图结构下模糊XML文档的特征,设计了一组高效的图结构模糊XML文档上的模式匹配算法。该算法基于一种适合于图结构文档的索引方式,采用自底向上的结点匹配顺序,大大减少了结点的重复判断操作,也不需要进行局部匹配结果的归并以及针对PC关系设计额外的过滤函数。理论分析以及实验结果证明,提出的模式匹配算法不仅在小枝查询性能上优于现有的相关算法,而且能够较好地实现DAG模式匹配查询。     

融入事件实体知识的汉越跨语言新闻事件检索

现有汉越跨语言新闻事件检索方法较少使用新闻领域内的事件实体知识,在候选文档中存在多个事件的情况下,与查询句无关的事件会干扰查询句与候选文档间的匹配精度,影响检索性能。提出一种融入事件实体知识的汉越跨语言新闻事件检索模型。通过查询翻译方法将汉语事件查询句翻译为越南语事件查询句,把跨语言新闻事件检索问题转化为单语新闻事件检索问题。考虑到查询句中只有单个事件,候选文档中多个事件共存会影响查询句和文档的精准匹配,利用事件触发词划分候选文档事件范围,减小文档中与查询无关事件的干扰。在此基础上,利用知识图谱和事件触发词得到事件实体丰富的知识表示,通过查询句与文档事件范围间的交互,提取到事件实体知识表示与词以及事件实体知识表示之间的排序特征。在汉越双语新闻数据集上的实验结果表明,与BM25、Conv-KNRM、ATER等基线模型相比,该模型能够取得较好的跨语言新闻事件检索效果,NDCG和MAP指标最高可提升0.712 2和0.587 2。     

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.     

基于互关联后继树的数学表达式检索

数学表达式结构复杂多样,给检索带来困难。为此,提出一种数学表达式索引与检索方法。在索引阶段,通过对LaTeX数学表达式特点的分析与归纳,定义面向表达式二维结构特性的数学表达式特征表示方式,将互关联后继树索引模型应用于数学表达式索引的构建,以解决树结构表示表达式的层次增长问题。在匹配阶段,设计包括精确匹配、相容匹配、子式匹配、模糊匹配等查询模式的匹配算法。在浏览器/服务器模式下采用51 076条数学表达式进行索引与匹配。实验结果表明,提出的方法可加快查询速度,减小索引存储空间,能够适应数学表达式的结构特点,取得较好的检索效果。     

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。     

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

 Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.     

Fuzzy query translation for relational database systems

The paper presents a new method for fuzzy query translation based on the alpha-cuts operations of fuzzy numbers. This proposed method allows the retrieval conditions of SQL queries to be described by fuzzy terms represented by fuzzy numbers. It emphasizes friendliness and flexibility for inexperienced users. The authors have implemented a fuzzy query translator to translate user's fuzzy queries into precise queries for relational database systems. Because the proposed method allows the user to construct his fuzzy queries intuitively and to choose different retrieval threshold values for fuzzy query translation, the existing relational database systems will be more friendly and more flexible to the users.     

A model of an information retrieval system with unbalanced fuzzy linguistic information

Most information retrieval systems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express the weights of queries and the relevance degrees of documents. However, to improve the system–user interaction, it seems more adequate to express these linguistic weights and degrees by means of unbalanced linguistic scales, that is, linguistic term sets with different discrimination levels on both sides of the middle linguistic term. In this contribution we present an information retrieval system that accepts weighted queries whose weights are expressed using unbalanced linguistic term sets. Then, the system provides the retrieved documents classified in linguistic relevance classes assessed on unbalanced linguistic term sets. To do so, we propose a methodology to manage unbalanced linguistic information and we use the linguistic 2‐tuple model as the representation base of the unbalanced linguistic information. Additionally, the linguistic 2‐tuple model allows us to increase the number of relevance classes in the output and also to improve the performance of the information retrieval system. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1197–1214, 2007.     

Linguistic aggregation operators of selection criteria in fuzzy information retrieval

A “softening” of the hard Boolean scheme for information retrieval is presented. In this approach, information retrieval is seen as a multicriteria decision-making activity in which the criteria to be satisfied by the potential solutions, i.e., the archived documents, are the requirements expressed in the query. the retrieval function is then an overall decision function evaluating the degree to which each potential solution satisfies a query consisting of information requirements aggregated by operators. Linguistic quantifiers and a connector dealing with primary and optional criteria are defined and introduced in the query language in order to specify the aggregation criteria of the single query requirements. These criteria make it possible for users to express queries in a simple and self-explanatory manner. In particular, linguistic quantifiers are defined which capture the intrinsic vagueness of information needs. © 1995 John Wiley & Sons, Inc.     

A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.     

Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval system

Information retrieval is an activity that attempts to produce documents that better fulfill user information needs. To achieve this activity an information retrieval system uses matching functions that specify the degree of relevance of a document with respect to a user query. Assuming linguistic‐weighted queries we present a new linguistic matching function for a threshold weighting semantics that is defined using a 2‐tuple fuzzy linguistic approach (Herrera F, Martínez L. IEEE Trans Fuzzy Syst 2000;8:746–752). This new 2‐tuple linguistic matching function can be interpreted as a tuning of that defined in “Modelling the Retrieval Process for an Information Retrieval System Using an Ordinal Fuzzy Linguistic Approach” (Herrera‐Viedma E. J Am Soc Inform Sci Technol 2001;52:460–475). We show that it simplifies the processes of computing in the retrieval activity, avoids the loss of precision in final results, and, consequently, can help to improve the users' satisfaction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 921–937, 2005.     

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

We present an information system developed to help assessing the microbiological risk in food. That information system contains experimental results in microbiology, mainly extracted from scientific publications. The increasing amount of the experimental results available and the difficulty to integrate them into a classic relational database schema led us to design a system composed of two distinct subsystems queried through a common interface. The first subsystem is a classic relational database. The second subsystem is a database containing weakly-structured pieces of information expressed in terms of conceptual graphs. The data stored in both bases can be fuzzy ones in order to take into account the specificities of the biological information. The uniform query language used on both relational database and conceptual graph database allows the users to express preferences by using fuzzy sets in their queries. The MIEL system is now operational and used by the microbiologists involved in the Sym’Previus French project.     

一种图像检索中的灰色相关反馈算法

在交互式CBIR系统中，由于用户的查询需求常常是模糊的，因此检索结果从某种意义上说是不确定的。于是，可以将图像检索过程视为一个“灰色系统”，其中的查询向量以及图像特征的权重可视为“灰数”。基于此，该文提出了一种新的相关反馈技术，它采用“灰关联分析”理论来分析和描述“例子图像”与“相关图像”之间的关系，据此自动更新查询向量与图像特征的权重，从而更准确地描述用户的查询需求。实验结果表明，这种相关反馈算法能较好地描述用户的查询需求，显著地改善了图像检索的性能。     

Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR

Information retrieval (IR) is the science of identifying documents or sub-documents from a collection of information or database. The collection of information does not necessarily be available in only one language as information does not depend on languages. Monolingual IR is the process of retrieving information in query language whereas cross-lingual information retrieval (CLIR) is the process of retrieving information in a language that differs from query language. In current scenario, there is a strong demand of CLIR system because it allows the user to expand the international scope of searching a relevant document. As compared to monolingual IR, one of the biggest problems of CLIR is poor retrieval performance that occurs due to query mismatching, multiple representations of query terms and untranslated query terms. Query expansion (QE) is the process or technique of adding related terms to the original query for query reformulation. Purpose of QE is to improve the performance and quality of retrieved information in CLIR system. In this paper, QE has been explored for a Hindi–English CLIR in which Hindi queries are used to search English documents. We used Okapi BM25 for documents ranking, and then by using term selection value, translated queries have been expanded. All experiments have been performed using FIRE 2012 dataset. Our result shows that the relevancy of Hindi–English CLIR can be improved by adding the lowest frequency term.