基于主题地图的电子政务信息资源组织研究

主题地图是一种新型的描述知识结构、知识与信息资源关联的ISO标准。从知识层面组织电子政务信息资源,主要就是构建电子政务信息资源的主题地图。步骤是:构建实现模型、准备数据资源、生成主题地图并进行校验。     

基于主题地图的高校学科信息组织模式研究

主题地图(Topic Map)是一种新的知识组织与揭示方法,并逐渐成为最佳的信息导航方法。在剖析主题地图概念及其描述语言的基础上,应用主题地图对学科专业领域信息资源内容进行组织,揭示专业领域内在的知识结构,构建基于主题地图的高校学科信息组织模式,从而实现对学科信息资源的有效组织。     

基于主题地图的电子政务隐性信息资源开发

以政府决策为载体的电子政务隐性信息资源开发,是政府对信息资源应用的“示范作用”的体现。以主题地图的主题、关联和事件要素为基础,开发出面向主题定位的导航目录、面向事件关联的信息空间和面向关联阐释的知识地图,从而形成面向应用的电子政务信息资源管理新模式。     

基于主题关联的馆藏数字资源多维语义标引研究

以主题关联为基础的多维语义标引方法可用于馆藏数字资源管理与导航,即通过构建馆藏数字资源多维语义标引体系来对不同数字资源知识对象间的语义关联进行标引,进而实现以内容主题关联为核心的馆藏数字资源组织与多维展示。与传统数据库检索相比,该方法更加准确、直观和有效,可以作为一种知识发现工具和技术应用于中小型机构知识库建设。     

基于战略地图的文献信息资源规划研究

文章阐述了战略地图的含义,并从明确需求差异、确定目标客户群及其需求内容、制定文献信息资源价值变化时间表、建立战略主题、完成战略资本准备、决定最终具体完成文献信息资源建设战略行动方案六个步骤规划战略地图在文献信息资源建设中的应用。     

基于主题图的旅游文献组织方法研究*

课题选取四川省阿坝州旅游文献信息资源作为研究个案,按照主题图（Topic Maps）的标准,分析旅游文献组织过程中主题及类型选取原则,定义旅游文献各种主题类型之间的关联关系,结合主题图工具软件提出旅游文献主题图构建方法,并展示旅游文献主题图的组织效果。     

面向知识服务的知识组织过程研究

文章通过分析传统知识组织不足,以解决用户问题为目标,在面向用户需求的基础上,通过数据、信息、文献等相互映射和语义关联,使数据、信息、文献等上升为能够实现知识服务的知识组织;通过用户知识需求分析、欲组织资源的分析、知识与资源的映射、知识组织结构设计、再生知识的产生、知识组织实现等形成一套面向知识服务的知识组织过程,为用户提供高效的知识服务.     

基于知识地图的企业知识组织设计研究

针对近年企业中的信息过剩以及知识资源凌乱分散、利用率不高的现象,提出一种基于知识地图的企业知识组织设计方法,包括业务流程分析、知识收集、知识分析、知识关联、知识索引、知识地图构建6个步骤,该方法可以有效地对企业知识进行组织,以知识地图的形式展现出来。以金融企业知识资源为例,给出实际的操作方法。     

主题图在旅游文献组织中的应用研究

旅游行业信息化不断发展变革的今天,提高旅游文献组织水平,不仅是旅游管理者、旅游开发人员和旅游从业人员充分发掘旅游资源的文化内涵、提升旅游产品的文化品位的重要手段,更是旅游业可持续发展的必然要求.由于<中图法>固有的缺陷导致旅游文献信息资源组织时面临诸多困难,文章选择四川省阿坝州旅游信息资源作为研究案例,提出利用主题地图(Topic Maps)来组织旅游文献的新方法.     

基于多元关系融合的科技文本主题识别方法研究

当前文本主题获取方法大多依靠单一关联分析,不能全面分析可获取信息,难以准确获取科技发展主题。科技文献的主题词、作者和引文之间蕴含了以研究主题内容为纽带的语义关联关系,主题词共现关系、引文关系和合著关系分别从不同的角度展现了主题关联关系。因此,本文根据主题词之间语义关系距离的远近,将主题识别中主题词关联分为基础关系、强化关系和新增关系,在此基础上提出面向主题识别的多元关系抽取及关系融合方法;并以基因工程疫苗的研发与制备领域为例进行领域实证分析,利用PathSelClus算法实现基于多元关系融合的主题聚类,通过对比实验证明多元关系融合可以有效提高实证领域的文本主题聚类效果,而未来多关系融合主题识别则是需要重点关注的问题。图4。表6。参考文献19。     

元数据与文献编目的异同

元数据与文献编目均是组织信息资源的方式,比较其异同,有利于用户对网上信息资源的存取与检索,有助于书目工作者参与网络信息的组织与控制.     

Information Retrieval from Documents: A Survey

Given the phenomenal growth in the variety and quantity of data available to users through electronic media, there is a great demand for efficient and effective ways to organize and search through all this information. Besides speech, our principal means of communication is through visual media, and in particular, through documents. In this paper, we provide an update on Doermann's comprehensive survey (1998) of research results in the broad area of document-based information retrieval. The scope of this survey is also somewhat broader, and there is a greater emphasis on relating document image analysis methods to conventional IR methods.Documents are available in a wide variety of formats. Technical papers are often available as ASCII files of clean, correct, text. Other documents may only be available as hardcopies. These documents have to be scanned and stored as images so that they may be processed by a computer. The textual content of these documents may also be extracted and recognized using OCR methods. Our survey covers the broad spectrum of methods that are required to handle different formats like text and images. The core of the paper focuses on methods that manipulate document images directly, and perform various information processing tasks such as retrieval, categorization, and summarization, without attempting to completely recognize the textual content of the document. We start, however, with a brief overview of traditional IR techniques that operate on clean text. We also discuss research dealing with text that is generated by running OCR on document images. Finally, we also briefly touch on the related problem of content-based image retrieval.     

A non-parametric topical relevance model

An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained in these top ranked documents from the initial search with the assumption that documents as a whole are relevant to the information need. However, in practice, documents are often multi-topical where only a portion of the documents may be relevant to the query. In this situation, exploitation of the topical composition of the top ranked documents, estimated with statistical topic modeling based approaches, can potentially be a useful cue to improve PRF effectiveness. The key idea behind our PRF method is to use the term-topic and the document-topic distributions obtained from topic modeling over the set of top ranked documents to re-rank the initially retrieved documents. The objective is to improve the ranks of documents that are primarily composed of the relevant topics expressed in the information need of the query. Our RF model can further be improved by making use of non-parametric topic modeling, where the number of topics can grow according to the document contents, thus giving the RF model the capability to adjust the number of topics based on the content of the top ranked documents. We empirically validate our topic model based RF approach on two document collections of diverse length and topical composition characteristics: (1) ad-hoc retrieval using the TREC 6-8 and the TREC Robust ’04 dataset, and (2) tweet retrieval using the TREC Microblog ’11 dataset. Results indicate that our proposed approach increases MAP by up to 9% in comparison to the results obtained with an LDA based language model (for initial retrieval) coupled with the relevance model (for feedback). Moreover, the non-parametric version of our proposed approach is shown to be more effective than its parametric counterpart due to its advantage of adapting the number of topics, improving results by up to 5.6% of MAP compared to the parametric version.     

Comparison and Classification of Documents Based on Layout Similarity

This paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. These fixed-length vectors are then compared to each other through a Manhattan distance computation for fast page layout comparison. The paper describes experiments and results to rank-order a set of document pages in terms of their layout similarity to a test document. We also demonstrate the usefulness of the features derived from interval coding in a hidden Markov model based page layout classification system that is trainable and extendible. The methods described in the paper can be used in various document retrieval tasks including visual similarity based retrieval, categorization and information extraction.     

Utilizing passage-based language models for ad hoc document retrieval

To cope with the fact that, in the ad hoc retrieval setting, documents relevant to a query could contain very few (short) parts (passages) with query-related information, researchers proposed passage-based document ranking approaches. We show that several of these retrieval methods can be understood, and new ones can be derived, using the same probabilistic model. We use language-model estimates to instantiate specific retrieval algorithms, and in doing so present a novel passage language model that integrates information from the containing document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we present yield passage language models that are more effective than the standard passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; these relevance models also outperform a document-based relevance model. Finally, we demonstrate the merits in using the document-homogeneity measures for integrating document-query and passage-query similarity information for document retrieval.     

Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database

This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments (General and Specific) and two categories of topics (Broad and Narrow). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call Coherent Retrieval Elements. The results of our experiments show that—when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics)—the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.     

Multilingual Information Retrieval Using Machine Translation, Relevance Feedback and Decompounding

Multilingual retrieval (querying of multiple document collections each in a different language) can be achieved by combining several individual techniques which enhance retrieval: machine translation to cross the language barrier, relevance feedback to add words to the initial query, decompounding for languages with complex term structure, and data fusion to combine monolingual retrieval results from different languages. Using the CLEF 2001 and CLEF 2002 topics and document collections, this paper evaluates these techniques within the context of a monolingual document ranking formula based upon logistic regression. Each individual technique yields improved performance over runs which do not utilize that technique. Moreover the techniques are complementary, in that combining the best techniques outperforms individual technique performance. An approximate but fast document translation using bilingual wordlists created from machine translation systems is presented and evaluated. The fast document translation is as effective as query translation in multilingual retrieval. Furthermore, when fast document translation is combined with query translation in multilingual retrieval, the performance is significantly better than that of query translation or fast document translation.     

文献呈现次序对用户相关性判断的影响

对文献相关性判断中的首因效应及近因效应进行实验研究,并对文献数量是否影响用户相关性判断进行探索。经实验研究后发现：用户相关性判断不仅受文献呈现次序的影响,还受文献检出数量的影响;相关性判断中的近因效应不如首因效应明显和普遍。希望此研究结果能为完善信息检索系统的设计及评估,为信息检索系统的设计更符合以用户为导向的理念提供一些借鉴。     

基于用户问题域的文献知识表述及检索系统的建立与实现

本文在提出文献知识单元概念的基础上,分析了面向用户问题域的文献知识本体,并构建了文献知识库的概念关系模型。针对具体的应用领域和应用目的,提出了基于语义描述的文献知识库元数据方案。结合现代信息理论和技术,建立了一个基于XML/RDF、面向知识创新的文献知识检索系统,并介绍了研究开发中的关键技术。     

网络环境下高校文献检索课改革探讨

本文分析了网络环境下信息资源的特点，指出了改革高校文献检索课是知识经济时代的呼唤，探讨了网络环境下高校文献检索课的教学改革。