共查询到20条相似文献，搜索用时 295 毫秒
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近年来,XML已成为信息表示、交换和发布的标准,因此,XML数据查询已成数据库(DB)和信息检索(IR)领域广发研究的问题。XML通常被视为一个标签树,常用的方法是近似查询。由于在用户查询通常有一定的关系约束出现,在分析了用户查询对XML元素和值关系约束之后,本文提出了使用关系约束近似提取XML元素的方法 相似文献
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介绍了XML的概念和一些特点，分析了XML和SQL Server2000数据交换的几种方式，以及在SQL Server2000中，使用SELECT语句可以把查询结果存储为XML文档，使用OPENXML语句提供了在关系数据库表中存储XML文档的功能，并实际实现了SQL Server2000和XML之间的数据交换。 相似文献
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本文致力于研究主动规则在新型数据库——XML原生数据库中的应用和实现。通过对主动规则各个部分的分析,详细论述了其在XML数据库中的应用。并通过XML Schema 数据模型构造XML原生数据库中的主动规则模型,通过XQuery查询语言实现XML原生数据库中主动规则的检索。 相似文献
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现有的XML查询技术效率不高,查询优化的研究成为业界热点和难点问题.本文基于路径表达式进行优化研究,设计了一个查询优化模型XQO,对XML查询过程进行查询解析、逻辑优化、物理优化.通过优化算法模型的设计优化策略,解决了海量XML查询优化存在的一些问题,并从实验结果对优化模型进行验证. 相似文献
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如何对XML文档进行有效管理与快速查询是当前学术界的研究热点,即所谓的XML数据库。对当前XML数据库的研究现状与发展趋势进行全面的论述与分析,以Oracle 10g数据库为例,简要介绍了Oracle XML DB技术,并以实例说明如何利用这种技术将XML和关系数据库联系起来,实现对XML文档的有效存储。 相似文献
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SQLScrver2005关系数据库管理系统提供了强大的管理XML数据的功能,但它用传统的通过建立索引等方式进行查询效果并不理想.文章分析了SQLserver2005在查询XML数据过程中低效的原因,提出了通过建立附加表或者附加列以及相应索引、利用查询窗口等措施来优化查询.实验结果表明,该方法能有效提高查询XML数据的效率. 相似文献
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该文着重研究了XML的索引结构，并对XML数据库的存取提出了自己的观点。构造了对XML这种半结构化文档建立索引和查询时采用的数据结构和算法。 相似文献
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讨论了XML与关系数据库的转换原理以及XML的一般存储模式,重点讲述了XML-RDB存储方法并对XML文档的存储效率进行了分析,最后介绍了XML的查询方法。 相似文献
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This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described. 相似文献
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As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries. 相似文献
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Francesco Colace Massimo De Santo Luca Greco Paolo Napoletano 《Information processing & management》2015
This paper proposes a novel query expansion method to improve accuracy of text retrieval systems. Our method makes use of a minimal relevance feedback to expand the initial query with a structured representation composed of weighted pairs of words. Such a structure is obtained from the relevance feedback through a method for pairs of words selection based on the Probabilistic Topic Model. We compared our method with other baseline query expansion schemes and methods. Evaluations performed on TREC-8 demonstrated the effectiveness of the proposed method with respect to the baseline. 相似文献
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搜索引擎中相关性反馈技术 总被引：10，自引：1，他引：10
As an important component of search engines, the relevance feedback system is very effective for improving the performance of search engines. This paper firstly reviews the history of relevance feedback technology in the past 30 years, then introduces 2 major methods in relevance feedback, i. e. term reweighting and query expansion, and discusses the relevance feedback technologies based on vector space model and statistical ranking model. 相似文献
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《Information processing & management》2001,37(3):521-541
We compare a user-defined passage feedback (pf) system to a document feedback (df) system. Df employed the adaptive linear model for retrieval, while pf used weighted query expansion based on positive and negative feedback. Twenty-four searchers performed the same six tasks in varying search and system-order per TREC-8 guidelines. We hypothesized that pf, which featured interactive query expansion, would outperform df, which relied on automatic query expansion. Initial analysis appeared to reject this hypothesis, as df showed slightly higher overall performance than pf. However, analysis by system-order groups indicates only the first pf use had lower performance. These data suggest that pf was more difficult to learn than df, though the second pf use yielded competitive performance. If performance of pf is indeed affected by learning, an improved pf system with usability enhancements may prove to be an effective mechanism for interactive information retrieval. 相似文献
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Francis C. Fernández-Reyes Jorge Hermosillo-Valadez Manuel Montes-y-Gómez 《Information processing & management》2018,54(1):1-13
The effectiveness of query expansion methods depends essentially on identifying good candidates, or prospects, semantically related to query terms. Word embeddings have been used recently in an attempt to address this problem. Nevertheless query disambiguation is still necessary as the semantic relatedness of each word in the corpus is modeled, but choosing the right terms for expansion from the standpoint of the un-modeled query semantics remains an open issue. In this paper we propose a novel query expansion method using word embeddings that models the global query semantics from the standpoint of prospect vocabulary terms. The proposed method allows to explore query-vocabulary semantic closeness in such a way that new terms, semantically related to more relevant topics, are elicited and added in function of the query as a whole. The method includes candidates pooling strategies that address disambiguation issues without using exogenous resources. We tested our method with three topic sets over CLEF corpora and compared it across different Information Retrieval models and against another expansion technique using word embeddings as well. Our experiments indicate that our method achieves significant results that outperform the baselines, improving both recall and precision metrics without relevance feedback. 相似文献
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We demonstrate effective new methods of document ranking based on lexical cohesive relationships between query terms. The proposed methods rely solely on the lexical relationships between original query terms, and do not involve query expansion or relevance feedback. Two types of lexical cohesive relationship information between query terms are used in document ranking: short-distance collocation relationship between query terms, and long-distance relationship, determined by the collocation of query terms with other words. The methods are evaluated on TREC corpora, and show improvements over baseline systems. 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
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The paper presents two approaches to interactively refining user search formulations and their evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12. The first method consists of asking the user to select a number of sentences that represent documents. The second method consists of showing to the user a list of noun phrases extracted from the initial document set. Both methods then expand the query based on the user feedback. The TREC results show that one of the methods is an effective means of interactive query expansion and yields significant performance improvements. The paper presents a comparison of the methods and detailed analysis of the evaluation results. 相似文献
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《Information processing & management》2005,41(3):457-473
Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in an approach often called structured query translation. In contrast, language models incorporate translation probabilities into a unified framework. We compare the two approaches on Arabic and Spanish data sets, using two kinds of bilingual dictionaries––one derived from a conventional dictionary, and one derived from a parallel corpus. We find that structured query processing gives slightly better results when queries are not expanded. On the other hand, when queries are expanded, language modeling gives better results, but only when using a probabilistic dictionary derived from a parallel corpus.We pursue two additional issues inherent in the comparison of structured query processing with language modeling. The first concerns query expansion, and the second is the role of translation probabilities. We compare conventional expansion techniques (pseudo-relevance feedback) with relevance modeling, a new IR approach which fits into the formal framework of language modeling. We find that relevance modeling and pseudo-relevance feedback achieve comparable levels of retrieval and that good translation probabilities confer a small but significant advantage. 相似文献