共查询到19条相似文献，搜索用时 593 毫秒
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基于Ajax技术特点,设计一个表现层与语义检索引擎的异步通信模型,解决语义检索中复杂对象的传递,通过“本体导航”实例验证该模型的可行性,从用户体验和冗余处理角度论证Ajax技术对改善语义检索性能的作用。 相似文献
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[目的/意义]构建国史知识检索平台,提高用户获取国史知识的效率,促进国史宣传和教育。[方法/过程]提出基于本体的国史知识检索平台构建思路与总体框架,在构建国史本体知识库的基础上,采用Neo4j数据库作为RDF数据仓储,创建基于Solr的实例索引、三元组索引和词条索引,针对多种检索需求设计实现检索引擎的执行流程、检索式构造方法以及查询处理算法,并为国史知识展示设计可视化实现方式。[结果/结论]构建国史知识检索平台,提供实体检索、查询问答、关联检索、时序检索及语义资源浏览等检索与浏览服务。该平台框架及关键技术实现方案可为面向领域知识的深度检索服务提供重要参考。 相似文献
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提出一种基于概念格的数字图书馆用户检索行为序列模式挖掘方法。该方法采用“基于概念格的自顶向下与分治相结合”的挖掘思想,通过自顶向下的概念格迭代,利用概念格的复用性和提取频繁项集的优势,获得数字图书馆用户检索行为的序列模式。该方法不需要遍历原始用户信息数据库,能够大大压缩挖掘时间,有助于数字图书馆提高用户检索速度、改进个性化服务。 相似文献
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汉语框架网络问答系统问句处理研究 总被引：1，自引：0，他引：1
问句处理是问答系统的首要问题。汉语框架网络问答系统旨在以汉语框架网络本体为基础,选择法律领域作为研究对象,进行问句处理的研究,探索新型的问答系统设计技术,来满足用户准确检索信息的需求。本论文利用依存关系表示查询问句的句法关系,并将查询问句与问句模板库中的模板进行匹配,最终确定查询问句的配价模式,实现对查询问句的框架语义标注,为下一步基于问答的框架语义检索系统的设计奠定基础。 相似文献
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本文提出了一种面向自然语言的智能检索系统框架。该框架的核心是语义推理和个性化处理。该文采用OWL描述的Ontology作为语义推理的基础，以检索请求统计库和用户模式库来实现用户的个性化检索，检索结果以知识的形式返回给用户，能极大地满足用户的信息需求。 相似文献
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基于查询扩展和词义消歧的语义检索 总被引：1，自引：1，他引：0
随着网络化信息的急剧增长以及自然语言固有的歧义性问题,当前基于关键字匹配的搜索引擎已不能满足信息搜索的需求,出现了"信息泛滥而知识缺乏"的现象.本文提出基于语义的智能搜索技术,利用WordNet和WordNet Domains知识库从结构相关性和领域相关性两个方面综合判定词义间的相关性.根据用户提交的查询关键词的整体相关性最大化原则来确定查询词义,进而进行查询扩展;同时对检索到的文档内容也进行语义消歧来去除无关文档,兼顾了查准率和查全率两个方面.模拟实验结果表明,本文方法的搜索性能较传统的关键字匹配法和一般的查询扩展方法有明显优势,检索精度分别提高了18%和28%. 相似文献
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梁锦华 《现代图书情报技术》1991,7(4):16-18
本文对比了两个文献主题数据库设计方案，并建立了实验模型进行了模拟运算，结果表明：“分段检索方案”在空间利用率和平均检索速度方面较优，而“记录号对照表检索方案”对文献的标引深度有较好的适应能力．本文还提出了一个主题词逻辑“与”运算的改进算法．改进后运算速度提高了5-10倍。 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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GoPubMed是基于PubMed的语义智能搜索引擎,Quertle是以PubMed为主要数据源的语义智能搜索引擎,两者都是基于本体向未来语义检索发展的尝试。GoPubMed最大的检索特色是对检索结果的分类统计和可视化。Quertle在本体技术和自然语言处理技术的支撑下,形成了Power TermTM检索,能识别单词大写时的特定含义,将检索提问与关系网进行匹配查找获得高度相关的检索结果。 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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针对常用信息检索模型存在的两大不足——检索提问与内容表达上的语义缺失与结果返回形式上的单文档局限,提出相应的解决方案,在此基础上进一步提出基于本体的族式返回检索模型,并就该模型中的部分关键问题,如族式返回、查询与文档表示以及语义匹配等进行讨论。 相似文献
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基于本体的查询扩展与规范 总被引：9，自引：0，他引：9
聂卉 《现代图书情报技术》2007,2(3):35-38
研究本体支持下的智能检索问题。利用语义的层次结构和蕴涵关联量化领域概念的关联程度实现查询扩展，并采用RDF的三元组方式规范检索关键词，依据本体中的关联重构用户查询需求，以匹配策略实现智能检索。经过实例计算与分析，验证该方法的合理性，可行性及特点。 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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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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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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Research on cross-language information retrieval (CLIR) has typically been restricted to settings using binary relevance assessments.
In this paper, we present evaluation results for dictionary-based CLIR using graded relevance assessments in a best match
retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the
tests. First, monolingual baseline queries were automatically formed from the topics. Secondly, source language topics (in
English, German, and Swedish) were automatically translated into the target language (Finnish), using structured target queries.
The effectiveness of the translated queries was compared to that of the monolingual queries. Thirdly, pseudo-relevance feedback
was used to expand the original target queries. CLIR performance was evaluated using three relevance thresholds: stringent,
regular, and liberal. When regular or liberal threshold was used, a reasonable performance was achieved. Using stringent threshold,
equally high performance could not be achieved. On all the relevance thresholds the performance of the translated queries
was successfully raised by pseudo-relevance feedback based query expansion. However, the performance of the stringent threshold
in relation to the other thresholds could not be raised by this method. 相似文献