共查询到20条相似文献，搜索用时 156 毫秒
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本文提出了一个基于语义网的个性化信息检索模型以实现用户的个性化信息检索。文章论述了该模型的模块组成，并对模型的功能模块进行了分析。 相似文献
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个性化信息检索系统的用户模型研究 总被引：1，自引：0，他引：1
分析了传统信息检索系统实现个性化信息检索的必然性,对个性化信息检索系统的信息代理Agent、用户模型等相关问题进行了探讨,提出了一个基于向量空间的个性化信息检索用户模型。 相似文献
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基于本体论的个性化信息检索 总被引：1，自引：0，他引：1
传统的网络信息检索存在很多缺陷,导致检索效率低下。提出了一种基于本体论的个性化网络信息检索的模型。该模型的实现可以在一定程度上缓解Internet网上信息过载的问题,提供用户个性化信息检索服务,提高网络信息检索的准确率。 相似文献
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Web信息的语义概念检索 总被引：5，自引：0，他引：5
文章分析了传统检索方法的不足，提出了一种新的基于语义概念的web信息检索系统。该模型利用自然语言处理技术，在语义层次上进行查询和检索，克服了传统检索方法的不足，提高了查全率与查准率。 相似文献
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向量空间模型信息检索技术讨论 总被引：9，自引：0，他引：9
传统的向量空间模型信息检索技术,只是简单地统计检索信息在文档中出现的频度,检索结果时常与文档不一致,没有反映出真实的相关性,提出了改进的加权算法,并借助辅助主题词表和个性化信息库设计了新的检索系统模型,改进了信息检索方法。 相似文献
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基于概念的Web信息检索 总被引：2，自引：3，他引：2
分析了传统检索方法的不足，提出了一种新的检索模型，即一种基于概念的Web信息检索系统。该模型利用自然语言处理技术，在语义层次上进行查询和检索，克服了传统检索方法的不足，提高了查全率与查准率。 相似文献
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在分析传统信息检索存在问题的基础上,论述语义网的有关关键技术,进而提出一种基于语义网的智能信息检索模型,并阐述实现其的关键技术. 相似文献
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基于本体匹配的语义对等网信息检索 总被引：2，自引：0，他引：2
提出了一种基于语义相似、本体匹配的对等网信息检索方法.定义语义节点,在节点中通过计算语义相似度,在网络中进行语义匹配来部分替换传统的字符串相似度计算.仿真模拟结果表明,该方法能够有效提高信息检索效率. 相似文献
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给出了本体论概念,对图书馆传统的信息检索机制进行了分析,实现本体构造的方法,提供了基于本体论进行信息检索,并构造了数字图书馆的检索模式的设想。 相似文献
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With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified. 相似文献
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叙词表转换为Ontology的研究 总被引：21，自引：3，他引：21
Ontology为需要共享某一领域信息的研究人员提供了通用的词表，而这正是传统的叙词表在信息检索中所起的作用。本文对叙词表向OntologY转换进行了综合性介绍，分别介绍了Ontology定义及作用、叙词表和Ontology的联系、Ontology所包含的内容和建立步骤以及叙词表转换为Ontology的研究和进展，最后结合一个具体实例加以阐述。 相似文献
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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。 相似文献
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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. 相似文献
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从信息资源的分类看搜索引擎的优化 总被引：1，自引：0，他引：1
The traditional infon-nation resources are classified according to scientificness and practicality. However,the classification of Internet information resources pays more attention to practicality while paying equal attention to scien-tificness. This article, starting from the classification methods of the traditional information resources and Intemet informs-lion resources, discusses how to optimize the search engines in classified catalogue retrieval and multiple keyword retrieval. 相似文献
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语义检索能克服传统的基于关键词匹配检索的缺点,是信息检索的发展趋势。本文主要探讨两种实现语义检索的索引:潜语义索引和其修正形式。首先介绍了潜语义索引的基本思想和检索过程,并在分析潜语义索引的不足的基础上,介绍了其修正形式———残差迭代变换。 相似文献
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Fatemeh Lashkari Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755
Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. 相似文献
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Jamal Abdul Nasir Iraklis Varlamis Samreen Ishfaq 《Information processing & management》2019,56(5):1605-1617
Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets. 相似文献