Remembering about documents: memory for appearance, format, and location

If we remember the visual appearance of documents, and other attributes such as location, then a number of new information management strategies become possible candidates for application in the design of filing systems. This paper describes a number of experiments aimed at investigating aspects of memory for documents in office settings. There is no evidence, as has previously been suggested, that automatic encoding for appearance or location of documents occurs at significant levels. The results of these experiments are more consistent with the view that visual and spatial attributes of documents are remembered in proportion to the attention paid to them when the documents are handled. The experiments also illustrate the sensitivity of this principle to the context in which subjects use documents. It is apparent that office tasks vary considerably in the extent to which subjects must pay attention to the visual and locational attributes of the documents handled. The consequences for the design of filing systems is discussed in terms of what methods for storage and retrieval can usefully be built into the design of systems.     

用自适应机制改进Web信息缓存管理的性能

目前,各种缓存（ｃａｃｈｉｎｇ）技术被广泛应用于Ｗｅｂ信息获取过程中,以求减少Ｉｎｔｅｒｎｅｔ的网络负载和提高响应速度,如何改进缓存技术从某种意义上成为制约Ｗｅｂ信息获取中的特点,然后提出了采用自适应机制改进Ｗｅｂ信息缓存管理性能的方法,同时给出了该方法的一些具体实现细节,该方法被应用于基于企业主题的Ｗｅｂ信息获取系统（ＷｅｂＣａｐｔｕｒｅ）的设计开发过程中,自适应机制的Ｗｅｂ信息缓存管理主要采用     

基于Markov网络及laplacian映射的快速相似性检索方法

快速相似性检索技术对于各种信息检索应用都具有很大的意义,其中基于语义哈希的快速相似性检索即是一个合理有效的检索方式,其检索模型能够在保证语义相关的基础上将高维空间中大量相关的文档数据,映射在低维空间中.虽然近年来许多关于语义哈希的研究都表现了不错的实验结果,但是都没有考虑到利用文档集合自身的信息来加强文档间的相关信息.为了有效利用文档自身信息,提出结合强化文档间邻接关系的马尔可夫迁移过程及使用保留局部信息的拉普拉斯映射方法的相似性检索方式.     

On the memorability of icons in an information retrieval task

Information retrieval involves the balance of two mnemonic processes: recognition of items presented to the user, and recall of where wanted documents might be. Iconic methods of human-computer interaction are seen to assist the recognition processes by virtue of the enrichment of cues provided. However, the principle of cue enrichment could apply equally to the process of recall, which is arguably a process more needing of support. This paper reports two exploratory experiments using icons to support the recall process in information retrieval. The results indicate no exceptional levels of recall. However, some aspects of users' performance suggest icons used in this way have some interesting and exploitable mnemonic properties. In particular, they may be useful in enhancing and supporting the search process by rapidly limiting the number of documents through which a user might be asked to search.     

Topic modelling of clickthrough data in image search

In this paper we explore the benefits of latent variable modelling of clickthrough data in the domain of image retrieval. Clicks in image search logs are regarded as implicit relevance judgements that express both user intent and important relations between selected documents. We posit that clickthrough data contains hidden topics and can be used to infer a lower dimensional latent space that can be subsequently employed to improve various aspects of the retrieval system. We use a subset of a clickthrough corpus from the image search portal of a news agency to evaluate several popular latent variable models in terms of their ability to model topics underlying queries. We demonstrate that latent variable modelling reveals underlying structure in clickthrough data and our results show that computing document similarities in the latent space improves retrieval effectiveness compared to computing similarities in the original query space. These results are compared with baselines using visual and textual features. We show performance substantially better than the visual baseline, which indicates that content-based image retrieval systems that do not exploit query logs could improve recall and precision by taking this historical data into account.     

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.     

LDA语义理解研究

潜在狄利克雷分配(LDA)被广泛应用于文本的聚类。有效理解信息检索的查询和文本,被证明能提高信息检索的性能。其中吉布斯采样和置信传播是求解LDA模型的两种热门的近似推理算法。比较了两种近似推理算法在不同主题规模下对信息检索性能的影响,并比较了LDA对文本解释的两种不同方式,即用文档的主题分布来替换原查询和文本,以及用文档的单词重构来替换原查询和文本。实验结果表明,文档的主题解释以及吉布斯采样算法能够有效提高信息检索的性能。     

Multiple coordinated views for searching and navigating Web content repositories

The advantages and positive effects of multiple coordinated views on search performance have been documented in several studies. This paper describes the implementation of multiple coordinated views within the Media Watch on Climate Change, a domain-specific news aggregation portal available at www.ecoresearch.net/climate that combines a portfolio of semantic services with a visual information exploration and retrieval interface. The system builds contextualized information spaces by enriching the content repository with geospatial, semantic and temporal annotations, and by applying semi-automated ontology learning to create a controlled vocabulary for structuring the stored information. Portlets visualize the different dimensions of the contextualized information spaces, providing the user with multiple views on the latest news media coverage. Context information facilitates access to complex datasets and helps users navigate large repositories of Web documents. Currently, the system synchronizes information landscapes, domain ontologies, geographic maps, tag clouds and just-in-time information retrieval agents that suggest similar topics and nearby locations.     

Concept mining for indexing medical literature

This article addresses the task of mining concepts from biomedical literature to index and search through a documents base. This research takes place within the Telemakus project, which has for goal to support and facilitate the knowledge discovery process by providing retrieval, visual, and interaction tools to mine and map research findings from research literature in the field of aging. A concept mining component automating research findings extraction such as the one presented here, would permit Telemakus to be efficiently applied to other domains. The main strategy that has been followed in this project has been to mine from the legends of the documents the research findings as relationships between concepts from the medical literature. The concept mining proceeds through stages of syntactic analysis, semantic analysis, relationships building, and ranking. Evaluation results are presented at the end and show that the system learns concepts and relationships between them with good recall, and that these concepts can be used for indexing the documents. Future improvements of the system are also presented.     

一种基于遗传算法的查询优化方法

为帮助用户在丰富的网络资源中快速、准确地查询到所需要的信息,提出一种基于遗传算法的查询优化方法.其基本思想是首先根据词项与所有查询词的共现程度在相关文档集合中选取扩展词对初始查询进行扩展,然后利用遗传算法为扩展后的查询选择优化的权重.实验结果表明,新方法具有更高的查全率和查准率.     

基于Ontology的信息检索技术研究

随着Web 的迅速发展,网上信息资源越来越丰富,网络已经成为了一个全球最大的信息库。而用户要从中得到所需的信息一般是通过各种信息检索工具。但是现有的信息检索工具都存在着检索精度不高等问题。本文针对这些问题,提出了将Ontology 融合到信息检索技术中的思路。利用Ontology 中拥有的领域知识,可以大大提高检索系统对自然语言文本的理解能力,同时方便用户以自然语言的方式提出检索请求,从而提高检索的效果。     

西安市数字方志全文检索系统的设计与实现

通过LuceneAPI实现对PDF文档的一次全文检索,为了更精确地定位搜索关键词,设计并实现了一种新的二次索引算法,该二次索引带有关键词的页码、坐标及其上下文等信息。利用该二次索引可将检索结果定位到PDF文档的具体页,然后在页面上标示出关键字的具体位置,使对PDF文档的二次检索达到了类似GoogleBook的图书检索效果。系统测试结果说明系统具有良好检索性能,有较高的查全率和查准率,能够满足用户快速检索的需求。系统作为西安市数字方志全文检索平台投入使用已有2年,取得了较好的应用成果。     

An interactive approach for CBIR using a network of radial basis functions

An important requirement for constructing effective content-based image retrieval (CBIR) systems is accurate characterization of visual information. Conventional nonadaptive models, which are usually adopted for this task in simple CBIR systems, do not adequately capture all aspects of the characteristics of the human visual system. An effective way of addressing this problem is to adopt a "human-computer" interactive approach, where the users directly teach the system about what they regard as being significant image features and their own notions of image similarity. We propose a machine learning approach for this task, which allows users to directly modify query characteristics by specifying their attributes in the form of training examples. Specifically, we apply a radial-basis function (RBF) network for implementing an adaptive metric which progressively models the notion of image similarity through continual relevance feedback from users. Experimental results show that the proposed methods not only outperform conventional CBIR systems in terms of both accuracy and robustness, but also previously proposed interactive systems.     

基于玉米本体的语义检索系统

采用形式概念分析方法由词汇-文件关系表构造概念格并进行约简,建立玉米种植本体。提出基于领域本体的语义标注方法,改进现有的权值计算方法以获得特征词,经句法分析生成RDF三元组。实现基于领域本体的用户查询处理和查询推荐算法,研制面向玉米种植的语义检索系统,并选取100篇玉米种植文档作为实验文本集合进行对比实验,结果表明,该语义检索系统在查准率和查全率上均优于基于关键字的检索方法。     

无人机空中通信数据库信息盲检索系统设计

对无人机空中通信数据库信息盲检索系统进行设计,能够有效解决传统盲检索系统存在的数据召回率低、细粒度差、检索准确度低及实时性差等问题。先给出无人机空中通信数据库信息盲检索系统的总体架构设计,通过对存储器结构进行改进,实现系统硬件部分的优化;采用Java语言和嵌入式开发库设计可视化检索页面,选取检索信息,增设中间件搜索功能,通过盲检索功能的实现,完成系统软件部分的开发,从而设计出无人机空中通信数据库信息盲检索系统。实验结果表明,该系统数据召回率高,细粒度强,检索准确度高,实时性好。     

A Task-Based Interface to Legal Databases

This paper addresses the problems that lawyers experience retrieving information from legal-text databases. Traditional access mechanisms of text databases require users to know how information is stored. We propose a method for index organisation which shields lawyers from the internal storage structures and which allows them to address the legal databases in their own legal terms. The proposed index is based on a model of legal tasks as opposed to traditional database indexes which represent the contents of the database. We will lay out the architecture of an information system in which this task model is used to determine the information need, to retrieve relevant documents and to give methodical guidance for the legal task itself. To account for the design of a task-based legal information retrieval system, a substantial part of this paper is devoted to analysis and representation of legal tasks.     

基于语义分析的查询扩展方法

查询扩展是优化信息检索的有效途径。为此,提出一种基于语义分析的查询扩展方法,利用基于互信息的共现模型分析初检文档,并将其作为部分扩展源,用模型的统计结果剪枝由语义词典WordNet生成的语义树,限制扩展范围。从初检文档和语义词典两方面选取扩展词对原查询进行扩展形成新的查询集。对返回结果进行重排序,调整前n篇文档的查准率。实验证明该方法是切实可行的。     

Performance analysis of three text-join algorithms

When a multidatabase system contains textual database systems (i.e., information retrieval systems), queries against the global schema of the multidatabase system may contain a new type of joins-joins between attributes of textual type. Three algorithms for processing such a type of joins are presented and their I/O costs are analyzed in this paper. Since such a type of joins often involves document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ on whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and the simulation results indicate that the relative performance of these algorithms depends on the input document collections, system characteristics, and the input query. For each algorithm, the type of input document collections with which the algorithm is likely to perform well is identified. An integrated algorithm that automatically selects the best algorithm to use is also proposed