A Study on Indexing Efficiency and Retrieval Accuracy for Author Name Search of Academic Papers

Most academic information has its creator, that is, a subject who has created the information. The subject can be an individual, a group, or an institution, and can be a nation depending on the nature of the relevant information. Most web data are composed of a title, an author, and contents. A paper which is under the academic information category has metadata including a title, an author, keyword, abstract, data about publication, place of publication, ISSN, and the like. A patent has metadata including the title, an applicant, an inventor, an attorney, IPC, number of application, and claims of the invention. Most web-based academic information services enable users to search the information by processing the meta-information. An important element is to search information by using the author field which corresponds to a personal name. This study suggests a method of efficient indexing and using the adjacent operation result ranking algorithm to which phrase search-based boosting elements are applied, and thus improving the accuracy of the search results of author name. This method can be effectively applied to providing accurate search results in the academic information services.

A New Indexing Method Based on Word Proximity for Chinese Text Retrieval

This paper proposed a novel text representation and matching scheme for Chinese text retrieval. At present, the indexing methods of Chinese retrieval systems are either character-based or word-based. The character-based indexing methods, such as bi-gram or tri-gram indexing, have high false drops due to the mismatches between queries and documents. On the other hand, it's difficult to efficiently identify all the proper nouns, terminology of different domains, and phrases in the word-based indexing systems. The new indexing method uses both proximity and mutual information of the word pairs to represent the text content so as to overcome the high false drop, new word and phrase problems that exist in the character-based and word-based systems. The evaluation results indicate that the average query precision of proximity-based indexing is 5.2% higher than the best results of TREC-5.

A distributed information retrieval method in peer-to-peer network

To avoid the scalability of the existing systems that employed centralized indexing,index flooding or query flooding,we proposed an efficient peer-to-peer information retrieval system SPIRS (Semantic P2P-based Information Retrieval System) that supported state-of-the-art content and semantic searches. SPIRS distributes document indices through P2P network hierarchically by Latent Semantic Indexing (LSI) and organizes nodes into a hierarchical overlay through CAN and TRIE. Comparing with other P2P search techniques,those based on simple keyword matching,SPIRS has better accuracy for considering the advanced relevance among documents. Given a query,only a small number of nodes are needed for SPIRS to identify the matching documents. Furthermore,both theoretical analysis and experimental results show that SPIRS possesses higher accuracy and less logic hops.

A comprehensive review of significant researches on content based indexing and retrieval of visual information

Developments in multimedia technologies have paved way for the storage of huge collections of video doc- uments on computer systems. It is essential to design tools for content-based access to the documents, so as to allow an efficient exploitation of these collections. Content based anal- ysis provides a flexible and powerful way to access video data when compared with the other traditional video analysis tech- niques. The area of content based video indexing and retrieval （CBVIR）, focusing on automating the indexing, retrieval and management of video, has attracted extensive research in the last decade. CBVIR is a lively area of research with endur- ing acknowledgments from several domains. Herein a vital assessment of contemporary researches associated with the content-based indexing and retrieval of visual information. In this paper, we present an extensive review of significant researches on CBV1R. Concise description of content based video analysis along with the techniques associated with the content based video indexing and retrieval is presented.

Supervised non-negative matrix factorization based latent semantic image indexing

A novel latent semantic indexing （LSI） approach for content-based image retrieval is presented in this paper. Firstly, an extension of non-negative matrix factorization （NMF） to supervised initialization is discussed. Then, supervised NMF is used in LSI to find the relationships between low-level features and high-level semantics. The retrieved results are compared with other approaches and a good performance is obtained.

A Full Text Retrieval System in a Digital Library Environment

The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.

MEXIR： An Implementation of High Performance and High Precision on XML Retrieval

Traditional information retrieval systems respond to user queries with ranked lists of relevant documents. Since, XML （Extensible Markup Language） documents separate content and structure; XML-IR （information retrieval） systems are able to retrieve only the relevant portions of documents. Therefore, users who utilize an XML-IR system could potentially receive highly relevant and precise material. We have developed the XML information retrieval system by using MySQL and Sphinx, which we call MEXIR. In our system, XML documents are stored into one table that has fixed relational schema. The schema is independent of the logical structure of XML documents. Each node in XML documents is represented by labels that express the positions in XML tree, namely ADXPI scheme. Our system has performance experiments on INEX collections and shown an average up to four seconds better than GPX. In addition, it has been reduced the size of the data down by 82.29 % compare to GPX system.

基于前缀剪枝的大规模向量空间相似检索框架

针对大规模文本集合下基于权重的相似性查询问题,提出一种支持前缀剪枝的高效检索框架。首先给出向量空间模型下相似性及其带权前缀定义,理论证明了带权前缀剪枝的正确性;其次,面向大规模文本查询,提出一种新的倒排索引结构,利用索引叶节点维护记录的前缀权重,并基于该索引构建高效的相似检索算法;最后,在TF/IDF权重策略下证明该方法能够有效支持大规模带权相似检索。结果表明,其查询效率较Lucene的归并验证策略提升了5倍以上。

基于H型指数的AI多维知识地图信息检索研究

当前的传统信息检索系统无法有效识别出AI地图的多维信息。为此,提出基于H型指数的AI多维知识地图信息检索方法。该方法对AI多维知识地图数据信息完成相似性计算,获取地图信息的相似度值,并结合H型指数获取地图信息的中心性指标;通过确定的检索边界以及选取的模型参数,构建地图信息的多维检索模型,通过模型求解结果,实现信息的检索。实验结果表明,该方法应用下,检索时间短,查全率、查准率均高于90%。

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

索引的本质属性及其在学术规范与评价中的作用

[目的/意义]索引是检索、统计、分析信息的利器,是图书情报学/信息资源管理研究的核心内容。数据库具有索引的功能,现代索引就是“数据库”。加强索引及数据库在学术规范与评价上的研究和实践,对于坚守图书情报学/信息资源管理的核心内容,适度扩展到相关学术领域或公共学术话题,具有重要意义。[研究设计/方法]利用概念分析法,探讨索引原理及其推论、索引与目录、目次和数据库的关系。利用典型案例分析法,分析各类型索引、数据库在学术规范和评价上的作用。[结论/发现]源文献抽取、注出出处、“异排”是索引区别于目录、目次等类似检索工具的“本质属性”。根据索引的原理,可以推出四大命题。索引及数据库在学术规范与评价方面有重要作用,但索引等技术手段仅是辅助工具。[创新/价值]提出“异排”一词来概括索引的独特性。同时概括出索引的本质属性及其原理和四大推论,率先将索引、数据库与学术规范与评价的关系明确地关联起来。

基于DeepMeSHⅡ模型的生物医学文献多标签分类

随着文本信息数据量迅速提升,多标签分类算法的开发研究逐渐成为热点。针对生物医学文献的文本内容,提出的DeepMeSHⅡ模型在目前最新进展DeepMeSH模型的基础上,将基于注意力机制的AttentionXML深度学习模型通过排序学习模型融合到该问题中,并利用期刊信息对模型进行特征改进。DeepMeSHⅡ模型应用效果很好,在生物医学文献语义索引国际比赛(BioSAQ2019)中取得了第一名的成绩。

基于ElasticSearch分布式搜索引擎的信息检索方法研究

随着信息量的骤增,传统关系型数据库很难做到实时高效地检索用户需要的信息,并且无法对信息进行分词及关键词组合的短文本搜索,从而很难优化信息检索结果的推荐展示。针对大量信息频繁检索的问题,采取一种基于ElasticSearch分布式搜索引擎,并采用分词器和倒排索引等技术,能较好地解决这些问题。同时在研究过程中利用代码实现了基础功能搜索、地理位置搜索以及通过算分排序推荐展示等功能。

隐含语义索引及其在中文文本处理中的应用研究

信息检索本质上是语义检索 ,而传统信息检索系统都是基于独立词索引 ,因此检索效果并不理想 .隐含语义索引是一种新型的信息检索模型 ,它通过奇异值分解 ,将词向量和文档向量投影到一个低维空间 ,消减了词和文档之间的语义模糊度 ,使得文档之间的语义关系更为明晰 .实验和理论结果证实了隐含语义索引能够取得更好的检索效果 .本文论述了隐含语义索引的理论基础 ,研究了隐含语义索引在中文文本处理中的应用 ,包括中文文本检索、中文文本分类和中文文本聚类等 .

中国城市形状的时空变化

基于国家资源环境数据库动态土地利用数据,计算了1990年和2000年我国31个大城市的形状指数。结果表明,从1934年～1990年的56年间城市形状从紧凑趋于非紧凑、分散;而在1990年～2000年10年间,由于受到城市建设用地控制增强的影响,城市形状趋于紧凑。从1934年～1974年间,在20个可比较的城市中有14个城市形状指数增加、6个城市形状指数减少。从1974年～1990年间,在22个可比较的城市中各有11个城市的形状指数增加和减少。从1990年～2000年间,31个城市中有11个城市的形状指数增加、20个城市形状指数减少。一些城市的形状指数在不同时间段变化十分剧烈。例如,兰州市是在1934年～1974年间形状指数增加最多的城市,而在1974年～1990年阶段则形状指数减少最多的城市。同样,武汉和昆明则是在1974年～1990年阶段形状指数增加量最大的城市,而在1990年～2000年阶段则是形状指数减少量最大的城市。这或许说明在城市规划和城市建设中,有时人们还未真正认识到城市形状的重要性,往往只有当城市形状变化到严重地影响人们的生产、生活和环境时,才发现形状存在的缺陷。

XML信息检索中最小子树根节点问题的分层算法

最小子树根节点问题(smallest lowest common ancestor,简称SLCA)是实现XML信息检索研究中关键字查询的一个基本问题,其主旨就是求解所有包含给定关键字的紧致子树的根节点.XU等人给出了3种算法—基于索引的搜索算法(indexed lookup eager,简称ILE)、基于堆栈的算法以及基于扫描的算法(scan eager,简称SE),并通过实验证明ILE算法具有最好的表现.与基于B+树索引结构的ILE算法不同,所给出的新算法,称为LISA(layered intersection scan algorithm)方法.该方法基于SLCA节点按“层”分布的规律,采取了逐层求解SLCA节点的思路,即在获取了包含关键字的节点的Dewey码集合后,通过计算对应于不同关键字、不同层次的Dewey码前缀集合的交集,可以得到对应不同层的SLCA节点.与ILE相比,LISA除了只需对应于关键字的节点集合信息以外,不再需要其他复杂的辅助数据结构——全部的信息只是对应不同关键字的Dewey码集合以及排序操作.同时,给出了两种实际的算法:LISAI和LISAII,二者的区别在于是否采用Dewey编码到整数的转换.其中,LISAII更具有满意的性能.

基于浅层语义分析技术的语义检索

在信息检索领域,语义检索技术较传统的关键字检索,无论在检索效果还是用户体验方面,都有诸多优势。语义检索融合了信息检索、语义分析以及信息融合等诸多方法,已成为现阶段该领域研究的一项重要技术。在Lu-cene索引技术基础之上,提出了语义检索的方法,即对语句进行语义分析,获得一种描述语句浅层语义信息的形式化表示,并对这种形式化表示建立索引;将表述语义联系的多层次相似度通过信息融合技术进行融合,并将其映射成查询语句与索引数据之间的相似度,达到语义检索的目的。

基于内容的图像检索综述

综述了基于内容图像检索领域中特征提取技术、多维索引与图像分类技术的研究成果 ,介绍了几个有特色的基于内容图像检索系统 ,最后提出了这一领域尚存在的问题和未来发展方向。

纯Peer to Peer环境下有效的Top-k查询

目前大多数的Peer-to-Peer(P2P)系统只支持基于文件标识的搜索,用户不能根据文件的内容进行搜索.Top-k查询被广泛地应用于搜索引擎中,获得了巨大的成功.可是,由于P2P系统是一个动态的、分散的系统,在纯的P2P环境下进行top-k查询是具有挑战性的.提出了一种基于直方图的分层top-k查询算法.首先,采用层次化的方法实现分布式的top-k查询,将结果的合并和排序分散到P2P网络中的各个节点上,充分利用了网络中的资源.其次,根据节点返回的结果为节点构建直方图,利用直方图估计节点可能的分数上限,对节点进行选择,提高了查询效率.实验证明,top-k查询提高了查询效果,而直方图则提高了查询效率.

一种面向产品创新设计的知识检索模型与实现

针对计算机辅助创新系统在中文知识检索方面的不足,提出一种基于共指关系扩展的知识检索方法。采用"共指关系索引—检索本体—设计知识"三层映射结构表达设计知识,形成一种以检索本体为组织核心、以共指关系索引为引导的面对面的知识结构体系。通过解析设计者以自然语言表达的检索意图,得到检索动名词词对,利用互信息技术计算概念术语间的相似度权重,匹配到符合检索意图的功能本体,调用已构建好的功能基索引检索设计知识。通过开发的知识检索原型系统中的检索示例验证了该方法的可行性。