基于重排序融合的社会图书检索系统

用图书的出版信息和用户生成的社会信息从社会媒体中搜索出相关的图书已成为信息检索系统的一个研究热点。然而大部分的信息检索系统都是由单一的检索方法构成,随着用户需求的不断增加,这些系统难以满足用户需求。针对上述问题,提出了一种基于重排序融合的图书检索系统。首先,使用伪相关反馈技术对用户查询内容进行扩展,并将检索结果作为初排序结果;其次,使用用户生成的社会信息特征对初排序结果进行重排序;最后,采用排序学习模型对多种重排序策略得到的结果进行融合。在INEX 2012-2014 Social Book Search公开数据集上针对其它先进检索系统进行了对比实验,实验结果表明,系统的性能（NDCG@10）优于其它方法构成的图书检索系统。     

Towards a graph-based user profile modeling for a session-based personalized search

Most Web search engines use the content of the Web documents and their link structures to assess the relevance of the document to the user’s query. With the growth of the information available on the web, it becomes difficult for such Web search engines to satisfy the user information need expressed by few keywords. First, personalized information retrieval is a promising way to resolve this problem by modeling the user profile by his general interests and then integrating it in a personalized document ranking model. In this paper, we present a personalized search approach that involves a graph-based representation of the user profile. The user profile refers to the user interest in a specific search session defined as a sequence of related queries. It is built by means of score propagation that allows activating a set of semantically related concepts of reference ontology, namely the ODP. The user profile is maintained across related search activities using a graph-based merging strategy. For the purpose of detecting related search activities, we define a session boundary recognition mechanism based on the Kendall rank correlation measure that tracks changes in the dominant concepts held by the user profile relatively to a new submitted query. Personalization is performed by re-ranking the search results of related queries using the user profile. Our experimental evaluation is carried out using the HARD 2003 TREC collection and showed that our session boundary recognition mechanism based on the Kendall measure provides a significant precision comparatively to other non-ranking based measures like the cosine and the WebJaccard similarity measures. Moreover, results proved that the graph-based search personalization is effective for improving the search accuracy.     

一种文献元数据搜索与共享系统

基于元搜索和Web信息抽取,介绍一种文献元数据搜索与共享系统,可提供统一的检索接口,对来自多个数据源的数据进行收集和整合,将文献按相关度排序,并实时查找文献信息及出处,提供基于文献元数据的共享与讨论平台,以便科研人员进行学术交流和协作。实验结果表明,该系统查询性能较好,可有效提高科研工作的效率。     

基于LDA主题模型的分布式信息检索集合选择方法

该文针对分布式信息检索时不同集合对最终检索结果贡献度有差异的现象,提出一种基于LDA主题模型的集合选择方法。该方法首先使用基于查询的采样方法获取各集合描述信息;其次,通过建立LDA主题模型计算查询与文档的主题相关度;再次,用基于关键词相关度与主题相关度相结合的方法估计查询与样本集中文档的综合相关度,进而估计查询与各集合的相关度;最后,选择相关度最高的M个集合进行检索。实验部分采用R_m、P@n和MAP作为评价指标,对集合选择方法的性能进行了验证。实验结果表明该方法能更准确的定位到包含相关文档多的集合,提高了检索结果的召回率和准确率。     

一种基于Lucene的Web全文信息检索系统的设计与实现

详细描述了利用Lucene全文索引工具包设计与实现的一个Web全文信息检索系统，给出了系统的设计框架和各个组成模块的实现技术，介绍了系统实现中的检索策略和算法。为了提高系统的检索性能。本文提出并实现了利用链入锚文本和链接分析对检索结果进行重新排序，有效提高了检索的准确率。     

一种基于文档相似度的检索结果重排序方法

对相关反馈问题的研究已有近30年的历史,相关反馈也被证明可以大程度稳定地提升检索系统的性能。当前网络环境下相关反馈的应用以及用户提供反馈信息的方式已经发生了明显的变化,因此相关反馈研究又一次引起了研究界的注意。该文提出了一种基于文档相似度的搜索结果重排序方法,该方法同时利用了反馈信息中的相关文档与不相关文档。在大规模网络信息检索标准实验数据上的实验结果表明:该方法不仅可以稳定地提高系统的检索性能,并且相较于经典的查询扩展方法有着明显的优势。     

The effects of multiple query evidences on social image retrieval

System performance assessment and comparison are fundamental for large-scale image search engine development. This article documents a set of comprehensive empirical studies to explore the effects of multiple query evidences on large-scale social image search. The search performance based on the social tags, different kinds of visual features and their combinations are systematically studied and analyzed. To quantify the visual query complexity, a novel quantitative metric is proposed and applied to assess the influences of different visual queries based on their complexity levels. Besides, we also study the effects of automatic text query expansion with social tags using a pseudo relevance feedback method on the retrieval performance. Our analysis of experimental results shows a few key research findings: (1) social tag-based retrieval methods can achieve much better results than content-based retrieval methods; (2) a combination of textual and visual features can significantly and consistently improve the search performance; (3) the complexity of image queries has a strong correlation with retrieval results’ quality—more complex queries lead to poorer search effectiveness; and (4) query expansion based on social tags frequently causes search topic drift and consequently leads to performance degradation.     

Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation

We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a ‘virtuous cycle’ between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous ‘Cranfield’ evaluation of Semantic Web search.     

On the search behaviour of users in the context of interactive social book search

ABSTRACT

Understanding the search behaviour of online users is among the long-tail practices of Interactive Information Retrieval that helps identify the user information needs. The Interactive Social Book Search (SBS), under the umbrella of Interactive Information Retrieval (IIR), aims to understand the user interactions with book collections and the associated professionally-curated and socially-constructed metadata on the baseline and multistage user interfaces (UIs). This paper reports on the book search behaviour of users by reviewing research publications related to the Interactive SBS published during the last two decades. It presents a holistic view of the overall progress of Interactive SBS by summarising and visualising the experimental structure, search systems, datasets, demographics of participants, and findings to identify the research trends and possible future directions. Based on the collected evidence, it attempts to answer how the search system, user interface (UI), and the nature of tasks affect the book search behaviour of users. The article is the first of its kind that attempts to understand the book search behaviour of users in the context of Social Book Search with implications for usability experts and others working in UI design, web search engines, book search engines, digital libraries, collaborative social cataloguing websites, and e-Commerce applications.     

基于多重增强图和主题分析的社交短文本检索方法

社会网络平台上的社交短文本不同于网页或其他文本,它的特点是内容短、文本间存在转发评论等关系、话题复杂多样、与Web页面有链接关系、文本的作者间有关注关系等,现有的检索系统不能完全适应。该文提出一个基于多重增强图的社交短文本检索方法SSTR,它利用多重增强图算法对通过Indri获得的初步检索结果实现再排序优化和去重。多重增强图算法是基于马尔科夫链理论设计出的图模型算法,社交短文本中蕴含的文本、作者、词语等不同层面的关系通过不同的图层及图中节点之间的边来建模。三个层面的关系相互增强,通过多次迭代运算,最终寻求多个层面间相互关系所处的稳定状态。多重增强图构建时,短文本的相似度计算基于主题分析结果,克服了传统余弦相似度计算时TF-IDF权重在短文本上的局限性。实验结果表明,与Indri、reRank-COS和reRank-LDA相比,基于多重增强图算法的SSTR排序的效果更好,适合初始检索结果相对较多的应用场合。     

基于主题词对的文档重排方法

信息检索中相关文档的排序一直是一个至关重要的问题。本文提出一种基于主题词对的文档重排方法,使得检索结果在保持召回率的前提下提高精确率。主题词对意指能够共同表征同一主题的两个词语,其中一个来自于查询,另一个来自于文档,两者之间具有紧密的联系。本文中,主题词对的选择采用概率潜在语义索引的方法,并根据主题词对在文档中的分布状况对其进行重排。对NTCIR-5中文信息检索的文档集合进行测试,采用trec标准评估方法,结果表明采用该方法使得精确率在rigid和relax结果集上分别提高了53.6% 和55.8%。     

Lost in PubMed. Factors influencing the success of medical information retrieval

With the explosion of information available on the Web, finding specific medical information in an efficient way has become a considerable challenge. PubMed/MEDLINE offers an alternative to free-text searching on the web, allowing searchers to do a keyword-based search using Medical Subject Headings. However, finding relevant information within a limited time frame remains a difficult task. The current study is based on an error analysis of data from a retrieval experiment conducted at the nursing departments of two Belgian universities and a British university. We identified the main difficulties in query formulation and relevance judgment and compared the profiles of the best and worst performers in the test.For the analysis, a query collection was built from the queries submitted by our test participants. The queries in this collection are all aimed at finding the same specific information in PubMed, which allowed us to identify what exactly went wrong in the query formulation step. Another crucial aspect for efficient information retrieval is relevance judgment. Differences between potential and actual recall of each query offered indications of the extent to which participants overlooked relevant citations.The test participants were divided into “worst”, “average” and “best” performers based on the number of relevant citations they selected: zero, one or two and three or more, respectively. We tried to find out what the differences in background and in search behavior were between these three groups.     

基于检索结果排序的伪相关反馈

针对传统伪相关反馈（PRF）算法扩展源质量不高使得检索效果不佳的问题,提出一种基于检索结果的排序模型（REM）。首先,该模型从初检结果中选择排名靠前的文档作为伪相关文档集;然后,以用户查询意图与伪相关文档集中各文档的相关度最大化、并且各文档之间相似性最小化作为排序原则,将伪相关文档集中各文档进行重排序;最后,将排序后排名靠前的文档作为扩展源进行二次反馈。实验结果表明,与两种传统伪反馈方法相比,该排序模型能获得与用户查询意图相关的反馈文档,可有效地提高检索效果。     

An image retrieval system with automatic query modification

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.     

Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrieval

Retrieving similar images based on its visual content is an important yet difficult problem. We propose in this paper a new method to improve the accuracy of content-based image retrieval systems. Typically, given a query image, existing retrieval methods return a ranked list based on the similarity scores between the query and individual images in the database. Our method goes further by relying on an analysis of the underlying connections among individual images in the database to improve this list. Initially, we consider each image in the database as a query and use an existing baseline method to search for its likely similar images. Then, the database is modeled as a graph where images are nodes and connections among possibly similar images are edges. Next, we introduce an algorithm to split this graph into stronger subgraphs, based on our notion of graph’s strength, so that images in each subgraph are expected to be truly similar to each other. We create for each subgraph a structure called integrated image which contains the visual features of all images in the subgraph. At query time, we compute the similarity scores not only between the query and individual database images but also between the query and the integrated images. The final similarity score of a database image is computed based on both its individual score and the score of the integrated image that it belongs to. This leads effectively to a re-ranking of the retrieved images. We evaluate our method on a common image retrieval benchmark and demonstrate a significant improvement over the traditional bag-of-words retrieval model.     

Social image retrieval based on topic diversity

Image search re-ranking is one of the most important approaches to enhance the text-based image search results. Extensive efforts have been dedicated to improve the accuracy and diversity of tag-based image retrieval. However, how to make the top-ranked results relevant and diverse is still a challenging problem. In this paper, we propose a novel method to diversify the retrieval results by latent topic analysis. We first employ NMF (Non-negative Matrix Factorization) Lee and Seung (Nature 401(6755):788–791, 1999) to estimate the initial relevance score to the query q. Then, the initial relevance score is fed into an adaptive multi-feature fusion model to learn the final relevance score. Next, the diversification process is conducted. We group all the images by semantic clustering and estimate the topic distribution of each cluster by topic analysis. The clusters are ranked based on the topic distribution vector and the final retrieval image list is obtained by a greedy selection mechanism based on the estimated relevances. Experimental results on the NUS-Wide dataset show the effectiveness of the proposed approach.

     

基于语义相关度排序的政务信息资源检索算法

政务信息资源检索是政务信息资源共享系统的重要功能。以《政务信息资源目录体系》国家标准中的XML元数据规范为依据,提出了一种支持关键词搜索的政务信息资源检索算法。该算法使用政务信息资源XML元数据的TF*IDF和关键词依赖度对检索结果集进行语义相关度排序,通过改进关键词倒排索引来提高检索效率。实验表明该算法在检索结果排序精确度和时间效率上均有较大的改善,可有效提高政务信息资源利用的数据共享服务能力。     

基于语义的Web信息检索

用户要从网络中得到所需的信息一般是通过各种搜索引擎。但是现有的搜索引擎都存在着检索相关度不高等问题。随着语义Web概念的提出及相关技术的发展,基于语义的Web信息检索逐渐成为了语义Web研究的热点。给出了传统搜索引擎存在的问题,从理论上分析了如何将语义Web技术融入Web信息检索中去,并在理论分析的基础上给出了基于语义的Web信息检索的模型。     

Semantic-driven multimedia retrieval with the MPEG Query Format

The MPEG Query Format (MPQF) is a new standard from the MPEG standardization committee which provides a standardized interface to multimedia document repositories. The purpose of this paper is describing the necessary extensions which will allow MPQF to manage metadata modelled with Semantic Web languages like RDF and OWL, and query constructs based on SPARQL. The suggested modifications include the definition of a new MPQF query type, and a generalization of the MPQF metadata processing model. As far as we know, this is the first work to apply the MPEG Query Format to semantic-driven search and retrieval of multimedia contents.     

从Web获取部分整体关系语料的方法

部分整体关系获取是知识获取中的重要组成部分。Web逐步成为知识获取的重要资源之一。搜索引擎是从Web中获取部分整体关系知识的有效手段之一,我们将Web中包含部分整体关系的检索结果集合称为部分整体关系语料。由于目前主流搜索引擎尚不支持语义搜索,如何构造有效的查询以得到富含部分整体关系的语料,从而进一步获取部分整体关系,就成为一个重要的问题。该文提出了一种新的查询构造方法,目的在于从Web中获取部分整体关系语料。该方法能够构造基于语境词的查询,进而利用现有的搜索引擎从Web中获取部分整体关系语料。该方法在两个方面与人工构造查询方法和基于语料库查询构造查询方法所获取的语料进行对比,其一是语料中含有部分整体关系的语句数量;二是从语料中进一步获取部分整体关系的难易程度。实验结果表明,该方法远远优于后两者。