Mobile Agents for Distributed and Heterogeneous Information Retrieval

The heterogeneous, distributed and voluminous nature of many government and corporate data sources impose severe constraints on meeting the diverse requirements of users who analyze the data. Additionally, communication bandwidth limitations, time constraints, and multiple data formats impose further restrictions on users of these distributed data sources. In this paper, we present an Agent-based Complex QUerying and Information Retrieval Engine (ACQUIRE) for large, heterogeneous, and distributed data sources. ACQUIRE acts as a softbot or interface agent by presenting users with a view of a single, unified, homogenous data source, against which users can pose high-level declarative queries. ACQUIRE translates each such user query into a set of sub-queries by employing a combination of planning and traditional database query optimization techniques. ACQUIRE then spawns a set of mobile agents corresponding to these sub-queries, which in turn retrieve the data from various distributed data sources by dynamically optimizing the retrieval strategy as it is carried out. These mobile agents carry with them data-processing code that can be executed at the remote site, thus reducing the size of data returned by the agent. When all mobile agents have returned, ACQUIRE filters and merges the retrieved data and presents the results to the user. While the system is still very much a work in progress, current validation experiments on simulated NASA Distributed Active Archive Centers (DAACs) have demonstrated that complex queries can be effectively decomposed and retrieved by this approach.     

Partial Collection Replication for Information Retrieval

The explosion of content in distributed information retrieval (IR) systems requires new mechanisms in order to attain timely and accurate retrieval of unstructured text. This paper shows how to exploit locality by building, using, and searching partial replicas of text collections in a distributed IR system. In this work, a partial replica includes a subset of the documents from larger collection(s) and the corresponding inference network search mechanism. For each query, the distributed system determines if partial replica is a good match and then searches it, or it searches the original collection. We demonstrate the scenarios where partial replication performs better than systems that use caches which only store previous query and answer pairs. We first use logs from THOMAS and Excite to examine query locality using query similarity versus exact match. We show that searching replicas can improve locality (from 3 to 19%) over the exact match required by caching. Replicas increase locality because they satisfy queries which are distinct but return the same or very similar answers. We then present a novel inference network replica selection function. We vary its parameters and compare it to previous collection selection functions, demonstrating a configuration that directs most of the appropriate queries to replicas in a replica hierarchy. We then explore the performance of partial replication in a distributed IR system. We compare it with caching and partitioning. Our validated simulator shows that the increases in locality due to replication make it preferable to caching alone, and that even a small increase of 4% in locality translates into a performance advantage. We also show a hybrid system with caches and replicas that performs better than each on their own.     

分布式情报检索系统的拓扑模型

本文建立了分布式情报检索系统的三种拓扑模型———检索拓扑 ,伪度量拓扑和相似性拓扑 ,并证明了检索拓扑与相似性拓扑具有一定的分布式特征 ,从而说明了这两种拓扑模型在分布式意义下的合理性。     

The Probability Ranking Principle Revisited

A theoretic framework for multimedia information retrieval is introduced which guarantees optimal retrieval effectiveness. In particular, a Ranking Principle for Distributed Multimedia-Documents (RPDM) is described together with an algorithm that satisfies this principle. Finally, the RPDM is shown to be a generalization of the Probability Ranking principle (PRP) which guarantees optimal retrieval effectiveness in the case of text document retrieval. The PRP justifies theoretically the relevance ranking adopted by modern search engines. In contrast to the classical PRP, the new RPDM takes into account transmission and inspection time, and most importantly, aspectual recall rather than simple recall.     

国际医学会议信息检索简介

医学会议信息是了解医学领域最新发展状况的一个重要情报源。论文介绍了几个国内外医学会议信息网站以及有效获取医学会议信息的一些技巧。     

基于XML的分布式信息检索

提出了一种对互联网信息进行分布式信息检索的方法：利用代理程序和XML技术向多个相同类型的网站同时发送检索请求文档并接收它们返回的检索结果文档，经过统一处理后将检索结果显示给读者     

高校图书馆的文献采访工作必须掌握动态信息

探讨了高校图书馆文献采访工作应如何掌握动态信息，提高购书质量，加强馆藏建设的原则以及网络环境下文献资源建设的新模式。     

基于移动代理的分布式信息检索

介绍了移动代理技术的内容和特点，分析了其性能优势。在此基础上，重点论述了基于移动代理技术的分布式信息检索的系统模型、系统实例、性能指标和移动代理规划。指出移动代理技术在分布式信息检索中的应用，是解决网络环境下分布式大量信息资源检索的有效途径。     

信息检索中修饰语作用的研究

在海量信息中检索时,与用户查询相关的信息常常被漏掉,而与查询无关的信息———信息垃圾,却大量地出现在检索结果中。改进文本信息检索系统的质量,提高检索效能,已成为亟待解决的问题。本文针对能够影响检索效力的一个易被忽略的因素———修饰语,研究其在文本信息检索中的作用。为此,构建了修正的向量空间模型(Modified Vector Space Model,MVSM),并以英文文本进行试验,进而说明修饰语的作用。     

基于Z39．50的分布式联机书目检索

Z9．50是网络信息检索的标准协议,目前主要应用于联机书目检索领域。本文首先探讨基于Z39．50的联机书目检索的技术以及目前面临的问题,然后提出一个应用查询路由技术的分布式结构,并讨论了MARC索引的组织,节点质心信息的提取,查询循环控制等关键技术。文中提出的系统结构和实现技术有助于提高数字图书馆环境中分布资源的查询效率。     

文检课为核心的信息素质教育模型的构建

阐述了韶关学院图书馆文检课的发展历程，特别是教学手段建设和教学方法改革方面取得的成效。随着课程体系的完善，新生教育和讲座的开展，学习网站和传统媒体的揉合，文检课为核心的信息素质教育模型业已凸显。     

Retrieving with Good Sense

Although always present in text, word sense ambiguity only recently became regarded as a problem to information retrieval which was potentially solvable. The growth of interest in word senses resulted from new directions taken in disambiguation research. This paper first outlines this research and surveys the resulting efforts in information retrieval. Although the majority of attempts to improve retrieval effectiveness were unsuccessful, much was learnt from the research. Most notably a notion of under what circumstance disambiguation may prove of use to retrieval.     

竞争情报系统中的信息收集与信息分析

首先从案例出发,介绍竞争情报系统的概念,并对其结构进行分析;然后简要地介绍竞争情报系统中信息研究的方法体系,最后对竞争情报系统中信息收集和信息分析这两个重要环节进行具体的论述。     

2006-2007年国外信息检索基本理论研究进展

2006-2007年国外对信息检索基础理论的研究主要集中于决策理论、隐含语义索引理论研究以及信息检索评价理论研究。关于信息检索基本原理的研究主要集中在信息检索中的分类、信息检索模型、信息检索类型和检索方式等方面。信息检索中的分类的研究重点包括有关分类器的研究;有关特征选择的研究;有关领域相关词的研究。信息检索类型的研究主要包括焦点检索、图像检索、视频检索、合作过滤、机器音译、无线网中网。检索方式的研究主要包括上下文检索、集成检索、问答系统检索以及用户查询处理等问题。     

A Bayesian Framework for XML Information Retrieval: Searching and Learning with the INEX Collection

Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents—which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents.     

宏检索及SDARTS协议探讨

宏检索的目标是实现跨多个分布式异构数据源的检索,并能对检索结果进行有效整合.SDARTS协议撷取了SDLIP和STARTS两个协议的精华部分,构建了宏检索的基本框架.本文介绍目前宏检索的研究现状,对SDARTS协议的特点及有待完善的地方作-探讨.     

从文献检索到信息检索——网络环境下检索课教学内容与方法的调整

从文献检索到信息检索最大的变化 :一是由文献单元向信息单元为基础的组织方式的改变 ;二是由手工分类、主题标引、著者标引经过机器的主题词、自由词抽取、标引发展到全文标引乃至超文本检索。网络技术、超媒体技术和智能技术等是促其变化的关键。作为一门学科的教学必须创建以CAI课件为主导的实践教学方法和建立信息检索课程的基本框架体系。参考文献 4。     

近几年国内信息检索可视化研究综述

文章首先简要介绍了国外信息检索可视化的研究现状,然后分析了国内近几年信息检索可视化的研究领域,包括理论探讨、技术开发及实际应用等几个方面。接着指出信息检索可视化发展所面临的问题。最后提出了信息检索可视化的未来展望。     

高校图书馆的文献信息服务

针对网络化条件下,高校图书馆用户需求的变化,提出了发展我国高校图书馆文献信息服务的若干对策。     

Using Corpus-Based Approaches in a System for Multilingual Information Retrieval

We present a system for multilingual information retrieval that allows users to formulate queries in their preferred language and retrieve relevant information from a collection containing documents in multiple languages. The system is based on a process of document level alignments, where documents of different languages are paired according to their similarity. The resulting mapping allows us to produce a multilingual comparable corpus. Such a corpus has multiple interesting applications. It allows us to build a data structure for query translation in cross-language information retrieval (CLIR). Moreover, we also perform pseudo relevance feedback on the alignments to improve our retrieval results. And finally, multiple retrieval runs can be merged into one unified result list. The resulting system is inexpensive, adaptable to domain-specific collections and new languages and has performed very well at the TREC-7 conference CLIR system comparison.