共查询到20条相似文献，搜索用时 671 毫秒
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为了提高文本挖掘的深度和精度,研究并提出了一种基于领域本体的语义文本挖掘模型.该模型利用语义角色标注进行语义分析,获取概念和概念间的语义关系,提高文本表示的准确度;针对传统的知识挖掘算法不能有效挖掘语义元数据库,设计了一种基于语义的模式挖掘算法挖掘文本深层的语义模式.实验结果表明,该模型能够挖掘文本数据库中的深层语义知识,获取的模式具有很强的潜在应用价值,设计的算法具有很强的适应性和可扩展性. 相似文献
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李燕波 《大学图书情报学刊》2013,(6):5-9,19
文章以SECI知识转化模式为分析框架,论述了OA对科研中知识创造、传递、存储和检索的影响.结果表明：OA加速了知识的创造,扩展了知识的传播范围,改变了知识存储与检索的模式,缩减了学术交流的成本.通过对比分析OA知识库的两种形式,即主题知识库相和机构知识库,指出前者在知识检索方面具有更高的效率. 相似文献
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依据语义检索的特征和文本概念的挖掘,通过楚辞研究数据库的语义实践,提出一种以本体知识库建设为核心,由本体开发、资源管理、检索服务三层架构组成,融语义词典、知识地图、跨库查询和专题搜索为一体的个性化关联语义检索模型,力图使当前的语义检索研究跳出实验的框架,促进相关领域文献知识的组织开发与检索利用。 相似文献
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[目的/意义] 在中医医案管理过程中存在中医医案元数据不统一、精度不足等问题,不利于中医医案知识库的信息检索、知识共享和挖掘,研究建立规范的中医医案元数据模型,对于支持中医医案的深度多维检索、知识共享和挖掘具有重要作用。[方法/过程] 通过文献调研法对现用中医相关标准规范、元数据标准进行总结;通过网络调查法收集中医医案实例,结合内容分析法对医案内容进行分析,初步界定医案元数据元素;再通过实地调查法,在中医诊疗现场对中医诊疗过程进行观察和访问,对初步界定的医案元数据元素进行优化,得到最终医案元数据元素集。[结果/结论] 构建了面向中医诊疗知识库的医案元数据模型,全面描述中医诊疗过程,为统一中医医案元数据提供参考。 相似文献
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读者知识库是读者有效知识的载体,是读者在利用图书馆的过程中所产生的一切信息的数据集合。读者知识库的构建和有效应用是全面推进知识服务与知识创新、提升图书馆服务效能的有效手段。从图书馆知识服务的角度看,读者知识库的应用模式可分为业务模式和研究模式两种。 相似文献
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数智时代,面对大数据、大知识所带来的挑战,如何创新发展信息分析方法,关乎新时代信息分析工作的开展,关乎数据资源的开发利用。本文在梳理现有信息分析方法的基础上,提出数据驱动、知识驱动,以及数据与知识融合驱动的三种数智型方法思路。首先,提出基于文本、网络、音频、图像等的数据驱动以及与之相应的文本挖掘、图挖掘、音频挖掘、图像挖掘等信息分析模式;其次,提出基于专家知识库、通用知识库、领域知识图谱、通用知识图谱等的知识驱动信息分析模式;最后,提出基于特征、模型、决策三种层面的数据与知识融合驱动的信息分析模式。通过以上三种方法,构建能够系统融合大数据、大知识的信息分析方法,实现数智融合型信息分析,促进图书情报学科方法论发展,赋能国家决策和社会治理。图3。表1。参考文献59。 相似文献
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本文从文本挖掘的定义着手,分析了文本挖掘的过程,包括文本预处理,文本知识发现,文本模式的评价以及文本模式的呈现,并详细介绍了文本挖掘在主动信息服务、信息检索系统、专利信息分析等方面的应用. 相似文献
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一种从医学文本中实现自动关键词抽取和筛选的技术方法* 总被引：2，自引：0，他引：2
鉴于重要关键词对于文本有着重要的强文本表示功能,关键词抽取和筛选在信息检索、信息抽取和知识挖掘等领域中有着重要的作用。在调研当前关键词抽取的方法后,结合医学领域已有的叙词表和工具以及BM25F加权词频公式提出基于医学文本的重要关键词抽取和筛选的技术方法。该方法主要解决两个关键问题：关键词的识别和抽取、关键词重要性的衡量和筛选。以2001-2007年骨关节炎领域的文献集合为数据来源,对该技术方法进行实践尝试,并验证其实际有效性,为知识挖掘中的重要关键词抽取提供一个行之有效的途径。 相似文献
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本文根据高校图书信息资料电子在线检索的特点，引入知识工程的树状知识表示理论，并结合检索过程中的关联规则，形成高校图书信息资料电子在线检索的知识库。提出一种基于知识库与数据库结合的数据搜寻与转化的方法，从而实现高校图书信息资料在线的个性化检索。 相似文献
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As the volume and variety of information sources continues to grow, there is increasing difficulty with respect to obtaining
information that accurately matches user information needs. A number of factors affect information retrieval effectiveness
(the accuracy of matching user information needs against the retrieved information). First, users often do not present search
queries in the form that optimally represents their information need. Second, the measure of a document’s relevance is often
highly subjective between different users. Third, information sources might contain heterogeneous documents, in multiple formats
and the representation of documents is not unified. This paper discusses an approach for improvement of information retrieval
effectiveness from document databases. It is proposed that retrieval effectiveness can be improved by applying computational
intelligence techniques for modelling information needs, through interactive reinforcement learning. The method combines qualitative
(subjective) user relevance feedback with quantitative (algorithmic) measures of the relevance of retrieved documents. An
information retrieval is developed whose retrieval effectiveness is evaluated using traditional precision and recall. 相似文献
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Bevan Koopman Guido Zuccon Peter Bruza Laurianne Sitbon Michael Lawley 《Information Retrieval》2016,19(1-2):6-37
This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem—the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search. 相似文献
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面向科技文献知识表示的知识元本体模型 总被引：1，自引：0，他引：1
[目的/意义]随着科技文献资源的急剧增长,用户淹没在科技文献的海洋中,为用户提供快速、精准的细粒度知识元服务将成为未来文献知识检索的发展趋势。[方法/过程]在分析科技文献文本结构的基础上,逐步深入到科技文献的内容中,以期通过构建一种面向科技文献知识表示的知识元本体模型,将科技文献内容中句义完整的细粒度知识点表示成具有统一结构的知识元。[结果/结论]以一篇科技文献为实例,展示笔者提出的基于知识元本体模型的科技文献知识表示方法,但该示例仅呈现了科技文献中引言部分的相关知识点,需进一步验证该知识元本体模型的有效性。 相似文献
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The heterogeneous, distributed and voluminous nature of many government and corporate data sources impose severe constraints on meeting the diverse requirements of users who analyze the data. Additionally, communication bandwidth limitations, time constraints, and multiple data formats impose further restrictions on users of these distributed data sources. In this paper, we present an Agent-based Complex QUerying and Information Retrieval Engine (ACQUIRE) for large, heterogeneous, and distributed data sources. ACQUIRE acts as a softbot or interface agent by presenting users with a view of a single, unified, homogenous data source, against which users can pose high-level declarative queries. ACQUIRE translates each such user query into a set of sub-queries by employing a combination of planning and traditional database query optimization techniques. ACQUIRE then spawns a set of mobile agents corresponding to these sub-queries, which in turn retrieve the data from various distributed data sources by dynamically optimizing the retrieval strategy as it is carried out. These mobile agents carry with them data-processing code that can be executed at the remote site, thus reducing the size of data returned by the agent. When all mobile agents have returned, ACQUIRE filters and merges the retrieved data and presents the results to the user. While the system is still very much a work in progress, current validation experiments on simulated NASA Distributed Active Archive Centers (DAACs) have demonstrated that complex queries can be effectively decomposed and retrieved by this approach. 相似文献
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针对Web信息检索现状和当前智能检索系统存在的问题，提出一个“先控”智能检索系统，面向基础用户，充分利用质量较高的网络资源分类目录体系，辅助形象化的“知识地图”显示，快速准确地定位用户的信息需求范畴，以提高检索效率和检索精度，同时分析了实现技术和尚待解决的问题。 相似文献
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分类检索是基于分类思想的一种信息检索模式,先后经历了早期的分类认知模式、传统的分类索引模式、网络分类导航模式、分类主题一体化模式、分类检索2.0模式。分类检索2.0模式将是信息分类检索的发展方向,并随着检索实践的专业化需求进一步走向规范化。 相似文献
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一种基于语义相似度和多属性决策方法的商品信息智能检索模型* 总被引：1，自引：0，他引：1
提出一种结合语义检索和多属性决策方法的商品信息检索模型。通过构建语义向量空间进行语义相似度计算,以实现检索结果与顾客查询关键词的语义匹配;同时该模型也采用TOPSIS多属性决策方法对检索到的商品进行效用值计算,从而建立商品内容的比较机制。最后,从准确率、顾客接受度等指标通过实验证实该模型的有效性,能够提高商品信息检索的精准度。 相似文献
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针对Web信息检索现状和当前智能检索系统存在的问题，提出一个“先控”智能检索系统，面向基础用户，充分利用质量较高的网络资源分类目录体系，辅助形象化的“知识地图”显示，快速准确地定位用户的信息需求范畴，以提高检索效率和检索精度，同时分析了实现技术和尚待解决的问题。 相似文献
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A structured document retrieval (SDR) system aims to minimize the effort users spend to locate relevant information by retrieving parts of documents. To evaluate the range of SDR tasks, from element to passage to tree retrieval, numerous task-specific measures have been proposed. This has resulted in SDR evaluation measures that cannot easily be compared with respect to each other and across tasks. In previous work, we defined the SDR task of tree retrieval where passage and element are special cases. In this paper, we look in greater detail into tree retrieval to identify the main components of SDR evaluation: relevance, navigation, and redundancy. Our goal is to evaluate SDR within a single probabilistic framework based on these components. This framework, called Extended Structural Relevance (ESR), calculates user expected gain in relevant information depending on whether it is seen via hits (relevant results retrieved), unseen via misses (relevant results not retrieved), or possibly seen via near-misses (relevant results accessed via navigation). We use these expectations as parameters to formulate evaluation measures for tree retrieval. We then demonstrate how existing task-specific measures, if viewed as tree retrieval, can be formulated, computed and compared using our framework. Finally, we experimentally validate ESR across a range of SDR tasks. 相似文献