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1.
Using EndNote version 7.0, the authors tested the search capabilities of the EndNote search engine for retrieving citations from MEDLINE for importation into EndNote, a citation management software package. Ovid MEDLINE and PubMed were selected for the comparison. Several searches were performed on Ovid MEDLINE and PubMed using EndNote as the search engine, and the same searches were run on both Ovid and PubMed directly. Findings indicate that it is preferable to search MEDLINE directly rather than using EndNote. The publishers of EndNote do warn its users about the limitations of their product as a search engine when searching external databases. In this article, the limitations of EndNote as a search engine for searching MEDLINE were explored as related to MeSH, non-MeSH, citation verification, and author searching. 相似文献
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Melissa Gomis Carole Gall Frances A. Brahmi 《Medical reference services quarterly》2013,32(3):260-271
ABSTRACT The authors of this article analyzed the differences in output when searching MEDLINE direct and MEDLINE via citation management software, EndNote X1®, EndNote Web®, and RefWorks©. Several searches were performed on Ovid MEDLINE and PubMed directly. These searches were compared against the same searches conducted in Ovid MEDLINE and PubMed using the search features in EndNote X1, EndNote Web, and RefWorks. Findings indicated that for in-depth research users, should search the databases directly rather than through the citation management software interface. The search results indicated it would be appropriate to search databases via citation management software for citation verification tasks and for cursory keyword searching. 相似文献
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The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match retrieval system (InQuery1). Query structure means the use of operators to express the relations between search keys. Six different structures were tested, representing strong structures (e.g., queries with facets or concepts identified) and weak structures (no concepts identified, a query is a bag of search keys). QE was based on concepts, which were first selected from a searching thesaurus, and then expanded by semantic relationships given in the thesaurus. The expansion levels were (a) no expansion, (b) a synonym expansion, (c) a narrower concept expansion, (d) an associative concept expansion, and (e) a cumulative expansion of all other expansions. With weak structures and Boolean structured queries, QE was not very effective. The best performance was achieved with a combination of a facet structure, where search keys within a facet were treated as instances of one search key (the SYN operator), and the largest expansion. 相似文献
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Transaction logs from online search engines are valuable for two reasons: First, they provide insight into human information-seeking behavior. Second, log data can be used to train user models, which can then be applied to improve retrieval systems. This article presents a study of logs from PubMed®, the public gateway to the MEDLINE® database of bibliographic records from the medical and biomedical primary literature. Unlike most previous studies on general Web search, our work examines user activities with a highly-specialized search engine. We encode user actions as string sequences and model these sequences using n-gram language models. The models are evaluated in terms of perplexity and in a sequence prediction task. They help us better understand how PubMed users search for information and provide an enabler for improving users’ search experience. 相似文献
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信息检索扩展技术研究 总被引：1，自引：0，他引：1
本文针对信息检索在查询扩展方面的不足,提出了一种结合本体理论和用户相关反馈技术的查询扩展方法。以FirteX作为检索平台, 选取WordNet作为本体扩展资源来验证本文所提出的查询扩展算法,实现结果表明该方法比基于余弦相似性的查询扩展方法在平均查全率、平均查准率方面有更大的优点。 相似文献
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Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document matrix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. However, for many knowledge domains there are pre-existing semantic structures that could be used to organize and categorize information. The goals of this study are (i) to demonstrate how such semantic structures can be automatically incorporated into the LSI vector space model, and (ii) to measure the effect of these structures on query matching performance. The new approach, referred to as Knowledge-Enhanced LSI, is applied to documents in the OHSUMED medical abstracts collection using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall data (11-point average precision values) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain (as much as 35%) over the original LSI for modest constraints on precision. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matches. 相似文献
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问答式信息检索是新一代搜索引擎,它接收自然语言描述的问题,在文档集合中搜索并返回问题的精确答案.问答式信息检索中,检索模块性能的提高将直接影响问题回答系统的整体性能.本文研究系统中的查询优化技术,包括两种策略:基于模式知识库的查询优化;挖掘Web语义蕴含信息,构建查询扩展资源.本文利用TREC提供的问题集与答案集(TREC8-TREC13)做实验来测试查询优化方法的性能,实验结果表明,相对于传统的查询生成,本文采用的查询优化技术在检索精度上取得了提高,t-test结果证明,系统性能提高统计显著. 相似文献
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Through casual observations, formal consultations, and educational sessions, the authors have identified various indexing features of the National Library of Medicine's Medical Subject Headings (MeSH) that pose challenges to end users while attempting to obtain relevant retrieval when searching MEDLINE. These problematic features include the use of Explodes, Tree structures, subheadings, Text Word vs. subject heading searching, and central concept searching. End-user search software is becoming more sophisticated with an increasing number of choices offered for search strategy formulation. Methods of instruction to orient the end user to these systems will also have to become more detailed. A review of the literature, that discusses end-user problems with using MEDLINE and MeSH, is included. 相似文献
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Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data. 相似文献
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特征词抽取和相关性融合的伪相关反馈查询扩展 总被引：2，自引：0，他引：2
针对现有信息检索系统中存在的词不匹配问题,提出一种基于特征词抽取和相关性融合的伪相关反馈查询扩展算法以及新的扩展词权重计算方法。该算法从前列n篇初检局部文档中抽取与原查询相关的特征词,根据特征词在初检文档集中出现的频度以及与原查询的相关度,将特征词确定为最终的扩展词实现查询扩展。实验结果表明,该方法有效,并能提高和改善信息检索性能。 相似文献
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Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain. 相似文献
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Anita A. H. Verhoeven Peter M. Boendermaker Edzard J. Boerma Betty Meyboom-De Jong 《Health information and libraries journal》1999,16(3):141-150
The aim of this study was to develop a model to evaluate the retrieval quality of search queries performed by Dutch general practitioners using the printed Index Medicus, MEDLINE on CD-ROM, and MEDLINE through GRATEFUL MED. Four search queries related to general practice were formulated for a continuing medical education course in literature searching. The selected potential relevant citations from the course instructor and the 103 course participants together served as the basic set for the three judges to evaluate for (a) relevance and (b) quality, with the latter based on journal ranking, research design and publication type. Relevant individual citations received a citation quality score from 1 (low) to 4 (high). The overall search quality was expressed in a formula, which included the individual citation quality score of the selected and missed relevant citations, and the number of selected non-relevant citations. The outcome measures were the number and quality of relevant citations and agreement between the judges. Out of 864 citations, 139 were assessed as relevant, of which 44 citations received an individual citation quality score of 1, 76 of 2, 19 of 3 and none of 4. The level of agreement between the judges was 68% for the relevant citations, and 88% for the non-relevant citations. We describe a model for the evaluation of search queries based not only on the relevance, but also on the quality of the citations retrieved. With adaptation, this model could be generalized to other professional users, and to other bibliographic sources. 相似文献
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《Medical reference services quarterly》2013,32(3):29-46
Through casual observations, formal consultations, and educational sessions, the authors have identified various indexing features of the National Library of Medicine's Medical Subject Headings (MeSH) that pose challenges to end users while attempting to obtain relevant retrieval when searching MEDUNE. These problematic features include the use of Explodes, Tree structures, subheadings, Text Word vs. subject heading searching, and central concept searching. End-user search software is becoming more sophisticated with an increasing number of choices offered for search strategy fomalation. Methods of instruction to orient the end user to these systems will also have to become more detailed. A review of the literature, that discusses end-user problems with using MEDLINE and MeSH, is included. 相似文献
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Lora V. Gault Mary Shultz Kathy J. Davies 《Journal of the Medical Library Association》2002,90(2):173-180
Objectives: This study compared the mapping of natural language patron terms to the Medical Subject Headings (MeSH) across six MeSH interfaces for the MEDLINE database.Methods: Test data were obtained from search requests submitted by patrons to the Library of the Health Sciences, University of Illinois at Chicago, over a nine-month period. Search request statements were parsed into separate terms or phrases. Using print sources from the National Library of Medicine, Each parsed patron term was assigned corresponding MeSH terms. Each patron term was entered into each of the selected interfaces to determine how effectively they mapped to MeSH. Data were collected for mapping success, accessibility of MeSH term within mapped list, and total number of MeSH choices within each list.Results: The selected MEDLINE interfaces do not map the same patron term in the same way, nor do they consistently lead to what is considered the appropriate MeSH term.Conclusions: If searchers utilize the MEDLINE database to its fullest potential by mapping to MeSH, the results of the mapping will vary between interfaces. This variance may ultimately impact the search results. These differences should be considered when choosing a MEDLINE interface and when instructing end users. 相似文献
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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
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调研UMLS构成和建设特点,重点研究UMLS在检索方面的应用实例,分析归纳UMLS在语义化、智能化检索方面的功能设计、实现方法与实际效果,以期为基于集成式知识组织系统的智能检索应用的场景功能设计、技术开发和实现,提供借鉴和参考。UMLS在智能检索中的应用主要包括:(1)扩展检索,主要有同义词扩展、等级结构扩展和词组切分扩展等方法;(2)语义检索,基于概念和概念之间的关系进行检索和结果内容表达;(3)问答式检索,包括问题分析、文献检索、语句提取、答案生成和语义聚类。 相似文献
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In the patent domain significant efforts are invested to assist researchers in formulating better queries, preferably via automated query expansion. Currently, automatic query expansion in patent search is mostly limited to computing co-occurring terms for the searchable features of the invention. Additional query terms are extracted automatically from patent documents based on entropy measures. Learning synonyms in the patent domain for automatic query expansion has been a difficult task. No dedicated sources providing synonyms for the patent domain, such as patent domain specific lexica or thesauri, are available. In this paper we focus on the highly professional search setting of patent examiners. In particular, we use query logs to learn synonyms for the patent domain. For automatic query expansion, we create term networks based on the query logs specifically for several USPTO patent classes. Experiments show good performance in automatic query expansion using these automatically generated term networks. Specifically, with a larger number of query logs for a specific patent US class available the performance of the learned term networks increases. 相似文献
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[目的/意义] 通过网页分类提高搜索引擎及内容网站的检索性能,根据查询意图分类更精确地满足用户需求。[方法/过程] 以信息类中文网页为研究对象,采用人工归纳的方法构建信息类查询意图类目体系,提出根据该类目体系对信息类网页进行分类的方法,并通过实验进行验证。[结果/结论] 实验结果表明,所提出的方法具有较强的可行性,有助于精确地满足用户信息需求,提高搜索引擎及内容网站的检索性能。 相似文献
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检索词自动扩展词库构建方法的基本思路是：根据语料是否规范化处理进行词库分类建设,优化了系统的检索性能;结合学科类别,对词库语料进行领域划分,引导科技人员对技术领域的准确把握;建设以本体库为基础,将与规范词具有关联性、相似性的语料通过关系表与关联库关联,把科技文献中的关键词组成一个有序的关系网,解决了传统检索系统中检索词无关联的不足;通过对检索词出现频率进行统计分析,进而更新词库,保证本体库、关联库语料的时效性,突破了人工对词库更新管理的受限性。 相似文献