基于哈希函数的查找算法设计及性能分析

查找是计算机程序设计中重要的操作,查找的效率一直是算法分析与设计领域关注的问题之一。在简要讨论传统的顺序查找和折半查找算法的基础上,提出一种基于哈希函数的查找算法。其方法综合了数字分析法和折叠法的特点。本文以图书信息查找为例,设计了查找算法并完成了性能分析。     

Effective ranking and search techniques for Web resources considering semantic relationships

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved.     

基于知识图谱的国际搜索引擎研究历程与热点演进

对国际上搜索引擎领域的研究文献进行比较细致、深入的考察,采用可视化方法进行定量分析并制作有关知识图谱,明晰国际搜索引擎研究的总体趋势和发展脉络。通过对高被引文献的深入解读和定性分析,以及近几年相关文献的共词分析,揭示国际上搜索引擎研究的起源和发展,以及从进入快速发展期至今四个阶段的研究热点演进历程。最后,总结和展望国际搜索引擎研究的热点方向。     

沟通对科技查新质量和效率的影响分析

通过对科技查新项目工作流程的协作交互关系的分析,阐述沟通在科技查新工作中的重要性。认为沟通是保证科技查新效率和质量的前提,提出将求教式提问、启发式提问、协商式提问的沟通方法引入查新工作中,以提高查新工作的效果。     

基于用户体验的可视化信息检索模型及界面研究

人类进入信息时代尤其是进入大数据时代后,信息数量急剧增长,传统信息检索方式由于其自身不足已经满足不了人们的要求。在用户体验为中心的时代,本文通过对比传统信息检索和可视化信息检索的概念、模型,提出了基于用户体验的可视化信息检索的概念、模型和检索界面,对于提高检索效率和准确性有一定的帮助。     

ClinicalAccess: A Clinical Decision Support Tool

ClinicalAccess is a new clinical decision support tool that uses a question-and-answer format to mirror clinical decision-making strategies. The unique format of ClinicalAccess delivers concise, authoritative answers to more than 120,000 clinical questions. This column presents a review of the product, a sample search, and a comparison with other point-of-care search engines.     

一类组合优化问题的智能算法研究

本文讨论的各类智能优化算法在组合优化问题中的应用，对它们的算法效率，收敛性作了讨论，并给出应用实例。     

中文数据库检索技术研究的一项新内容 兼谈《美国国会图书馆拼音转换计划:新汉语罗马化准则》的诞生及其主要内容

文章在简单论述从威妥玛拼音产生到汉语拼音诞生的全过程基础之上,首次在国内全面阐述和分析了<美国国会图书馆拼音转换计划>的主要内容,并且详细、准确地介绍了威氏标音和汉语拼音的对照表,以及美国国会图书馆对于中国传统地名由威氏标音向汉语拼音转换的具体标目修改形式和修改时间.这对于我国中文数据库检索技术的研究和全球信息资源共享来说是相当重要的.这是西方图书馆史上的一个新的里程碑.     

搜索引擎的商业行为分析与思考

随着搜索引擎相互之间竞争的加剧，许多搜索引擎开始选择商业化发展路线。在本文中，分析了当前搜索引擎商业化发展出现的原因，探讨了搜索引擎商业活动的主要模式，并对未来搜索引擎商业发展中出现的问题提出初步的思考。     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.