共查询到20条相似文献，搜索用时 359 毫秒
1.
[目的/意义]了解、分析和识别用户学术搜索时所表达的信息需求是优化查询结果、提高学术搜索引擎用户体验的首要步骤,而用户进行学术搜索时通过查询表达式所表达的用户表意信息需求及潜在信息需求可称之为学术查询意图。本文总结学术查询意图类目体系有助于学术查询意图识别和检索结果页面的呈现。[方法/过程]在A.Broder的查询意图类目体系的基础上,结合百度学术搜索查询日志中查询表达式实例,构建学术查询意图的类目体系。以此为基础,总结不同类别的学术查询意图,并分析不同类别学术查询意图下查询表达式的特点。[结果/结论]学术查询意图主要分为学术文献类、学术实体类、学术探索类、知识问答类和非学术文献类五大类;得出不同类别学术查询意图在学术搜索中的大致比例;给出每类学术查询意图的查询表达式特征、查询情景和查询结果页。 相似文献
2.
[目的 /意义]构建基于用户评论的图书分面体系和图书查询意图的分面检索模型,提升用户图书检索体验。[方法/过程]在调研大规模图书评论数据的基础上,立足图书评论数据特征进行细粒度图书分面体系构建,在此基础上,引入查询意图识别模块来构建图书分面检索模型,并进行原型系统的实现以验证模型的可行性和效果。[结果 /结论 ]通过原型系统的实现证实了所构建的细粒度分面体系能够有效帮助用户筛选和定位图书检索结果;提出的分面检索模型操作便捷,并能够结合用户的查询意图有效减少信息过载的问题,具有良好的用户体验。 相似文献
3.
基于领域本体的专利信息检索系统研究与实现 总被引：1，自引：0，他引：1
针对传统信息检索方法在当今网络信息环境下所面临的问题,提出基于领域本体的专利信息检索模型,从用户检索请求处理、本体构建、本体可视化与语义扩展、检索及存储的过程和技术实现进行研究,并开发一个基于服装领域本体的专利信息检索原型系统。比较测试表明,该模型在确保信息检索准确性的同时能够极大地提高其全面性。 相似文献
4.
针对当前书目检索过程中缺少检索建议与提示而影响检索性能的现状,进行检索建议与提示策略的研究。通过阐述检索行为的概念与属性、分析用户的检索心理,挖掘用户行为数据,并在此基础上实施访问OPAC网站、输入检索词、获得检索结果及选择检索结果等检索过程与行为的引导服务与查询帮助,从而较为准确地判断用户的查询意图,对用户的检索行为给出实时的、丰富的检索建议与提示,以期增强书目检索功能,提高系统的互动性,提升用户的查询体验。 相似文献
5.
EBSCOhost推出可视化检索 总被引：1，自引：0，他引：1
苍宏宇 《图书情报工作动态》2008,(2):33
由于全世界愈来愈多的用户在日常工作中会使用到EBSCO host的数据库,所以个性化检索方式需要有所改变。为了满足不同的检索需求,EBSCO提供了各种已扩展的数据库,及具有强大功能的工具有效地在海量数据中进行检索,例如可视化检索用户界面,其设计了两种形式来帮助初学者,并为用户呈现图形化的检索结果。可视化检索用户可以方便地从深度和广度两方面来全面了解结果集,避免了在结果集中跳转页面的麻烦。 相似文献
6.
[目的/意义]实现学术查询意图的自动识别,提高学术搜索引擎的效率。[方法/过程]结合已有查询意图特征和学术搜索特点,从基本信息、特定关键词、实体和出现频率4个层面对查询表达式进行特征构造,运用Naive Bayes、Logistic回归、SVM、Random Forest四种分类算法进行查询意图自动识别的预实验,计算不同方法的准确率、召回率和F值。提出了一种将Logistic回归算法所预测的识别结果扩展到大规模数据集、提取"关键词类"特征的方法构建学术查询意图识别的深度学习两层分类器。[结果/结论]两层分类器的宏平均F1值为0.651,优于其他算法,能够有效平衡不同学术查询意图的类别准确率与召回率效果。两层分类器在学术探索类的效果最好,F1值为0.783。 相似文献
7.
针对语义检索在实际应用中面临的用户查询意图获取困难、潜在语义索引计算复杂、领域本体覆盖范围小、概念语义类型不丰富、自动化程度低等问题,提出基于WordNet和SUMO本体集成的自动语义检索及可视化模型。实验表明这种模型能够过滤掉大量与用户查询无关的信息,提高信息检索系统的检准率,并很好地满足用户可视化和个性化检索需求。 相似文献
8.
9.
分析CBIR系统的用户查询模式以及基于形状特征的检索系统构建相关技术,在系统构建中应用示例图像与采样图像两种方式对图像的形状特征进行检索,通过图像分割获取目标轮廓,利用轮廓点与兴趣点之间的空间分布关系构造形状描述函数,应用傅立叶变换提取图像特征,最后在系统检索实验中证明其有效性。 相似文献
10.
移动图书馆WAP和APP用户检索行为比较分析 总被引：1，自引：0，他引：1
[目的/意义] 对比用户在使用WAP和APP这两种方式访问移动图书馆时的检索行为,为移动图书馆的服务创新提供参考。[方法/过程] 通过对某高校图书馆OPAC系统移动端日志数据进行统计分析,从搜索会话、查询式、高频关键词以及检索方式等方面展开研究。[结果/结论] 发现用户更多地是使用WAP访问移动图书馆,相比之下,在使用APP访问移动图书馆时,用户更倾向于在短时间内进行较少的查询来结束搜索会话;使用这两种方式查询的高频关键词所属领域有很大的相似性,中文检索多集中在数学、管理学、经济学、社会学等领域;简单检索是用户访问移动图书馆时使用的主要检索方式,通过WAP访问的用户选择其他检索方式的比率要大于通过APP访问的用户。 相似文献
11.
基于Sogou实验室提供的查询日志数据和新闻数据,探讨潜在时间意图查询的判断及其相关时间属性识别,构建潜在时间意图查询的检索排序模型。实验结果表明,时间属性识别的准确率为85%,且构建的检索模型能有效提高排序效果。 相似文献
12.
Query recommendation has long been considered a key feature of search engines, which can improve users’ search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user’s initial query. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users’ information needs. For this purpose, we attempt to infer query utility from users’ sequential search behaviors recorded in their search sessions. Specifically, we propose a dynamic Bayesian network, referred as Query Utility Model (QUM), to capture query utility by simultaneously modeling users’ reformulation and click behaviors. We then recommend queries with high utility to help users better accomplish their search tasks. We empirically evaluated the performance of our approach on a publicly released query log by comparing with the state-of-the-art methods. The experimental results show that, by recommending high utility queries, our approach is far more effective in helping users find relevant search results and thus satisfying their information needs. 相似文献
13.
E. Herrera-Viedma A. G. López-Herrera S. Alonso J. M. Moreno F. J. Cabrerizo C. Porcel 《Information Retrieval》2009,12(2):179-200
This paper describes a computer-supported learning system to teach students the principles and concepts of Fuzzy Information
Retrieval Systems based on weighted queries. This tool is used to support the teacher’s activity in the degree course Information Retrieval Systems Based on Artificial Intelligence at the Faculty of Library and Information Sciences at the University of Granada. Learning of languages of weighted queries
in Fuzzy Information Retrieval Systems is complex because it is very difficult to understand the different semantics that
could be associated to the weights of queries together with their respective strategies of query evaluation. We have developed
and implemented this computer-supported education system because it allows to support the teacher’s activity in the classroom
to teach the use of weighted queries in FIRSs and it helps students to develop self-learning processes on the use of such
queries. We have evaluated the performance of its use in the learning process according to the students’ perceptions and their
results obtained in the course’s exams. We have observed that using this software tool the students learn better the management
of the weighted query languages and then their performance in the exams is improved.
相似文献
| C. PorcelEmail: |
14.
In Information Retrieval, since it is hard to identify users’ information needs, many approaches have been tried to solve
this problem by expanding initial queries and reweighting the terms in the expanded queries using users’ relevance judgments.
Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it
is not always available. Another solution is to use correlated terms for query expansion. The main problem with this approach
is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study,
we try to construct query concepts that denote users’ information needs from a document space, rather than to reformulate initial queries using the term correlations
and/or users’ relevance feedback. To form query concepts, we extract features from each document, and then cluster the features into primitive concepts that are then used to form
query concepts. Experiments are performed on the Associated Press (AP) dataset taken from the TREC collection. The experimental evaluation
shows that our proposed framework called QCM (Query Concept Method) outperforms baseline probabilistic retrieval model on
TREC retrieval. 相似文献
15.
User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users’ various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is α-nDCG@5 0.307, IA-P@5 0.121, and α#-nDCG@5 0.214 on the TREC09, as well as α-nDCG@10 0.421, IA-P@10 0.201, and α#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users’ search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics. 相似文献
16.
The collective feedback of the users of an Information Retrieval (IR) system has been shown to provide semantic information
that, while hard to extract using standard IR techniques, can be useful in Web mining tasks. In the last few years, several
approaches have been proposed to process the logs stored by Internet Service Providers (ISP), Intranet proxies or Web search
engines. However, the solutions proposed in the literature only partially represent the information available in the Web logs.
In this paper, we propose to use a richer data structure, which is able to preserve most of the information available in the
Web logs. This data structure consists of three groups of entities: users, documents and queries, which are connected in a
network of relations. Query refinements correspond to separate transitions between the corresponding query nodes in the graph,
while users are linked to the queries they have issued and to the documents they have selected. The classical query/document
transitions, which connect a query to the documents selected by the users’ in the returned result page, are also considered.
The resulting data structure is a complete representation of the collective search activity performed by the users of a search
engine or of an Intranet. The experimental results show that this more powerful representation can be successfully used in
several Web mining tasks like discovering semantically relevant query suggestions and Web page categorization by topic. 相似文献
17.
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. 相似文献
18.
Armin Hust 《Informatik - Forschung und Entwicklung》2005,19(4):224-238
Information Retrieval Systeme haben in den letzten Jahren nur geringe Verbesserungen in der Retrieval Performance erzielt.
Wir arbeiten an neuen Ans?tzen, dem sogenannten Collaborativen Information Retrieval (CIR), die das Potential haben, starke
Verbesserungen zu erreichen. CIR ist die Methode, mit der durch Ausnutzen von Informationen aus früheren Anfragen die Retrieval
Peformance für die aktuelle Anfrage verbessert wird. Wir haben ein eingeschr?nktes Szenario, in dem nur alte Anfragen und
dazu relevante Antwortdokumente zur Verfügung stehen. Neue Ans?tze für Methoden der Query Expansion führen unter diesen Bedingungen
zu Verbesserungen der Retrieval Performance .
The accuracy of ad-hoc document retrieval systems has reached a stable plateau in the last few years. We are working on so-called
collaborative information retrieval (CIR) systems which have the potential to overcome the current limits. We define CIR as
a task, where an information retrieval (IR) system uses information gathered from previous search processes from one or several
users to improve retrieval performance for the current user searching for information. We focus on a restricted setting in
CIR in which only old queries and correct answer documents to these queries are available for improving a new query. For this
restricted setting we propose new approaches for query expansion procedures. We show how CIR methods can improve overall IR
performance.
CR Subject Classification H.3.3 相似文献
19.
In many probabilistic modeling approaches to Information Retrieval we are interested in estimating how well a document model
“fits” the user’s information need (query model). On the other hand in statistics, goodness of fit tests are well established
techniques for assessing the assumptions about the underlying distribution of a data set. Supposing that the query terms are
randomly distributed in the various documents of the collection, we actually want to know whether the occurrences of the query
terms are more frequently distributed by chance in a particular document. This can be quantified by the so-called goodness
of fit tests. In this paper, we present a new document ranking technique based on Chi-square goodness of fit tests. Given
the null hypothesis that there is no association between the query terms q and the document d irrespective of any chance occurrences, we perform a Chi-square goodness of fit test for assessing this hypothesis and calculate
the corresponding Chi-square values. Our retrieval formula is based on ranking the documents in the collection according to
these calculated Chi-square values. The method was evaluated over the entire test collection of TREC data, on disks 4 and
5, using the topics of TREC-7 and TREC-8 (50 topics each) conferences. It performs well, outperforming steadily the classical
OKAPI term frequency weighting formula but below that of KL-Divergence from language modeling approach. Despite this, we believe
that the technique is an important non-parametric way of thinking of retrieval, offering the possibility to try simple alternative
retrieval formulas within goodness-of-fit statistical tests’ framework, modeling the data in various ways estimating or assigning any arbitrary theoretical distribution
in terms. 相似文献