共查询到20条相似文献，搜索用时 484 毫秒
1.
本文利用模糊继承机制,对数据库结构中概念模式进行扩展。在保证多个IR系统数据独立性基础上,形成一共同映射。检索过程中,参照共同映射将查询表达成继承式的传递到各IR系统,在这过程中,不影响各数据库结构的外模式。 相似文献
2.
Meriem Amina Zingla Chiraz Latiri Philippe Mulhem Catherine Berrut Yahya Slimani 《Information Retrieval》2018,21(4):337-367
Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different configurations, starting from methods based on association rules and combining it with external knowledge. The HQE model handles the two main phases of a QE process, namely: the candidate terms generation phase and the selection phase. We propose for the first phase, statistical, semantic and conceptual methods to generate new related terms for a given query. For the second phase, we introduce a similarity measure, ESAC, based on the Explicit Semantic Analysis that computes the relatedness between a query and the set of candidate terms. The performance of the proposed HQE model is evaluated within two experimental validations. The first one addresses the tweet search task proposed by TREC Microblog Track 2011 and an ad-hoc IR task related to the hard topics of the TREC Robust 2004. The second experimental validation concerns the tweet contextualization task organized by INEX 2014. Global results highlighted the effectiveness of our HQE model and of association rules mining for QE combined with external resources. 相似文献
3.
Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data. 相似文献
4.
Latent Semantic Indexing (LSI) is a popular information retrieval model for concept-based searching. As with many vector space IR models, LSI requires an existing term-document association structure such as a term-by-document matrix. The term-by-document matrix, constructed during document parsing, can only capture weighted vocabulary occurrence patterns in the documents. However, for many knowledge domains there are pre-existing semantic structures that could be used to organize and categorize information. The goals of this study are (i) to demonstrate how such semantic structures can be automatically incorporated into the LSI vector space model, and (ii) to measure the effect of these structures on query matching performance. The new approach, referred to as Knowledge-Enhanced LSI, is applied to documents in the OHSUMED medical abstracts collection using the semantic structures provided by the UMLS Semantic Network and MeSH. Results based on precision-recall data (11-point average precision values) indicate that a MeSH-enhanced search index is capable of delivering noticeable incremental performance gain (as much as 35%) over the original LSI for modest constraints on precision. This performance gain is achieved by replacing the original query with the MeSH heading extracted from the query text via regular expression matches. 相似文献
5.
基于多层次概念语义网络结构的中文医学信息语义标引体系和语义检索模型研究 总被引：18，自引：0，他引：18
为了提高中文医学信息检索效率,本文应用语义学研究成果,深入剖析统一医学语言系统(UMLS),从理论上对多层次概念语义网络结构进行了探讨,以此设计了适用于中文医学信息特点的三层概念语义网络结构,并分别确定了各个概念语义网络层次的语义类型和语义关系,进一步完善了医学信息语义网络.以信息检索的认知理论为依据,建立了基于三层概念语义网络结构的中文医学信息语义标引体系和语义检索模型.对扩展检索和语义检索进行统计学Kappa检验,认为两种检索方法的一致性非常显著(p<0.01);与扩展检索中的任何一种方法相比,语义检索方法具有更高的检索效率. 相似文献
6.
利用查询术语同义词关系扩展信念网络检索模型 总被引：2，自引：0，他引：2
信念网络模型是一种重要的、基于贝叶斯网络的信息检索模型.它定义了一个明确的样本空间,给出了信息检索的一个灵活有效的基本框架.本文针对传统信念网络模型没有利用术语之间关系的缺陷,利用信息检索用同义词和词语相似度等概念,提出了最优同义词、相似概念、概念相似度等定义,提出了一种概念相似度的计算方法.然后利用上述定义对传统信念网络模型进行扩展,提出了一种基于查询术语同义词关系的扩展信念网络检索模型,讨论了扩展模型的拓扑结构和利用扩展模型进行信息检索的具体方法.实验结果表明,扩展后的信念网络模型比传统模型具有更好的检索性能. 相似文献
7.
提出基于关联数据技术组织用户需求的设想及其架构——需求语义网络模型,该模型由数据层、需求信息层、应用层组成,需求信息层是整个模型的核心,其构建包括需求信息建模、需求信息命名、需求信息RDF化、需求信息发布、开放查询5个步骤,需求语义网络构建的重点和难点包括用户需求及关系的定义与描述、用户需求的关联与分解、需求网络中各层次之间的协作与交流以及匹配服务器的延伸和扩展等,最后,将需求语义网络理论应用到高校图书馆个性化知识服务中,提出基于关联数据的高校图书馆图书需求语义网络的构建模型。 相似文献
8.
Improving English and Chinese Ad-Hoc Retrieval: A Tipster Text Phase 3 Project Report 总被引：1，自引：1，他引：0
K.L. Kwok 《Information Retrieval》2000,3(4):313-338
Both English and Chinese ad-hoc information retrieval were investigated in this Tipster 3 project. Part of our objectives is to study the use of various term level and phrasal level evidence to improve retrieval accuracy. For short queries, we studied five term level techniques that together can lead to good improvements over standard ad-hoc 2-stage retrieval for TREC5-8 experiments. For long queries, we studied the use of linguistic phrases to re-rank retrieval lists. Its effect is small but consistently positive.For Chinese IR, we investigated three simple representations for documents and queries: short-words, bigrams and characters. Both approximate short-word segmentation or bigrams, augmented with characters, give highly effective results. Accurate word segmentation appears not crucial for overall result of a query set. Character indexing by itself is not competitive. Additional improvements may be obtained using collection enrichment and combination of retrieval lists.Our PIRCS document-focused retrieval is also shown to have similarity with a simple language model approach to IR. 相似文献
9.
检索词自动扩展词库构建方法的基本思路是：根据语料是否规范化处理进行词库分类建设,优化了系统的检索性能;结合学科类别,对词库语料进行领域划分,引导科技人员对技术领域的准确把握;建设以本体库为基础,将与规范词具有关联性、相似性的语料通过关系表与关联库关联,把科技文献中的关键词组成一个有序的关系网,解决了传统检索系统中检索词无关联的不足;通过对检索词出现频率进行统计分析,进而更新词库,保证本体库、关联库语料的时效性,突破了人工对词库更新管理的受限性。 相似文献
10.
Relevance feedback methods generally suffer from topic drift caused by word ambiguities and synonymous uses of words. Topic drift is an important issue in patent information retrieval as people tend to use different expressions describing similar concepts causing low precision and recall at the same time. Furthermore, failing to retrieve relevant patents to an application during the examination process may cause legal problems caused by granting an existing invention. A possible cause of topic drift is utilizing a relevance feedback-based search method. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with phrases disambiguating the original query words. The idea was implemented for patent search where patents are classified into a hierarchy of categories, and the analyses of the experimental results showed not only the positive roles of phrases and words in retrieving additional relevant documents through query expansion but also their contributions to alleviating the query drift problem. More specifically, our query expansion method was compared against relevance-based language model, a state-of-the-art query expansion method, to show its superiority in terms of MAP on all levels of the classification hierarchy. 相似文献
11.
Information Retrieval with a Hybrid Automatic Query Expansion and Data Fusion Procedure 总被引：1，自引：0，他引：1
We propose a hybrid information retrieval (IR) procedure that builds on two well-known IR approaches: data fusion and query expansion via relevance feedback. This IR procedure is designed to exploit the strengths of data fusion and relevance feedback and to avoid some weaknesses of these approaches. We show that our IR procedure is built on postulates that can be justified analytically and empirically. Additionally, we offer an empirical investigation of the procedure, showing that it is superior to relevance feedback on some dimensions and comparable on other dimensions. The empirical investigation also verifies the conditions under which the use of our IR procedure could be beneficial. 相似文献
12.
[目的/意义]用户的检索式行为是用户信息搜索的重要环节,本文拟通过综述的形式对这些研究进行梳理,形成较为完整的综述。[方法/过程]通过对国内外相关文献的梳理,将检索式构建行为划分为检索词、检索式和会话层三个层面,以及词汇、语法和语义三个维度,对每个维度及不同维度之间的研究的区别与联系进行讨论,并对检索式的重构行为、检索式的质量和效果评估、以及影响用户检索式行为的要素等几个方面的相关研究进行总结。[结果/结论]已有研究对于检索式行为中的检索词和检索式的词汇研究较为丰富,未来需要增加对检索式的语法和语义的研究,以便深入理解用户的检索式构成特征。另外,关于检索式重构的类别和模式的自动识别的探索有所不足。在检索式的质量和效果评估方面,还需结合多种情境因素,更深入地研究易于用户理解和利于其搜索的检索式推荐模式。 相似文献
13.
We propose a method for search privacy on the Internet, focusing on enhancing plausible deniability against search engine query-logs. The method approximates the target search results, without submitting the intended query and avoiding other exposing queries, by employing sets of queries representing more general concepts. We model the problem theoretically, and investigate the practical feasibility and effectiveness of the proposed solution with a set of real queries with privacy issues on a large web collection. The findings may have implications for other IR research areas, such as query expansion and fusion in meta-search. Finally, we discuss ideas for privacy, such as k-anonymity, and how these may be applied to search tasks. 相似文献
14.
Prior-art search in patent retrieval is concerned with finding all existing patents relevant to a patent application. Since patents often appear in different languages, cross-language information retrieval (CLIR) is an essential component of effective patent search. In recent years machine translation (MT) has become the dominant approach to translation in CLIR. Standard MT systems focus on generating proper translations that are morphologically and syntactically correct. Development of effective MT systems of this type requires large training resources and high computational power for training and translation. This is an important issue for patent CLIR where queries are typically very long sometimes taking the form of a full patent application, meaning that query translation using MT systems can be very slow. However, in contrast to MT, the focus for information retrieval (IR) is on the conceptual meaning of the search words regardless of their surface form, or the linguistic structure of the output. Thus much of the complexity of MT is not required for effective CLIR. We present an adapted MT technique specifically designed for CLIR. In this method IR text pre-processing in the form of stop word removal and stemming are applied to the MT training corpus prior to the training phase. Applying this step leads to a significant decrease in the MT computational and training resources requirements. Experimental application of the new approach to the cross language patent retrieval task from CLEF-IP 2010 shows that the new technique to be up to 23 times faster than standard MT for query translations, while maintaining IR effectiveness statistically indistinguishable from standard MT when large training resources are used. Furthermore the new method is significantly better than standard MT when only limited translation training resources are available, which can be a significant issue for translation in specialized domains. The new MT technique also enables patent document translation in a practical amount of time with a resulting significant improvement in the retrieval effectiveness. 相似文献
15.
16.
一种面向用户兴趣的个性化语义查询扩展方法 总被引：1，自引：0，他引：1
在基于本体的语义查询扩展研究的基础上,结合用户模型的研究,提出要将用户的兴趣模型与查询扩展相结合,实现个性化的语义查询扩展,并把个性化的语义查询扩展过程分为两个阶段——检索关键词向用户模型中的个性化领域本体概念的映射以及在本体层次对映射概念的语义扩展,给出每一阶段的实现算法。实验表明该方法能够提高信息检索的查准率和查全率,在一定程度上满足个性化的查询需求。 相似文献
17.
Query Expansion with Long-Span Collocates 总被引：1，自引：0，他引：1
The paper presents two novel approaches to query expansion with long-span collocates—words, significantly co-occurring in topic-size windows with query terms. In the first approach—global collocation analysis—collocates of query terms are extracted from the entire collection, in the second—local collocation analysis—from a subset of retrieved documents. The significance of association between collocates was estimated using modified Mutual Information and Z score. The techniques were tested using the Okapi IR system. The effect of different parameters on performance was evaluated: window size, number of expansion terms, measures of collocation significance and types of expansion terms. We present performance results of these techniques and provide comparison with related approaches. 相似文献
18.
The effects of query structures and query expansion (QE) on retrieval performance were tested with a best match retrieval system (InQuery1). Query structure means the use of operators to express the relations between search keys. Six different structures were tested, representing strong structures (e.g., queries with facets or concepts identified) and weak structures (no concepts identified, a query is a bag of search keys). QE was based on concepts, which were first selected from a searching thesaurus, and then expanded by semantic relationships given in the thesaurus. The expansion levels were (a) no expansion, (b) a synonym expansion, (c) a narrower concept expansion, (d) an associative concept expansion, and (e) a cumulative expansion of all other expansions. With weak structures and Boolean structured queries, QE was not very effective. The best performance was achieved with a combination of a facet structure, where search keys within a facet were treated as instances of one search key (the SYN operator), and the largest expansion. 相似文献
19.
Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between
the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC
Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting
the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect
to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework
that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness
of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome
of these experiments, we discuss the success factors required for effective query expansion with respect to various sources
of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and
domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important
factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find
that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline
system.
相似文献
| Nicola StokesEmail: Email: |
20.
Query reformulation mining: models,patterns, and applications 总被引：1，自引：0，他引：1
Paolo Boldi Francesco Bonchi Carlos Castillo Sebastiano Vigna 《Information Retrieval》2011,14(3):257-289
Understanding query reformulation patterns is a key task towards next generation web search engines. If we can do that, then
we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time,
and thus helping users locate information more effectively and improving their web-search experience. As a step in this direction,
we build a very accurate model for classifying user query reformulations into broad classes (generalization, specialization,
error correction or parallel move), achieving 92% accuracy. We then apply the model to automatically label two very large
query logs sampled from different geographic areas, and containing a total of approximately 17 million query reformulations.
We study the resulting reformulation patterns, matching some results from previous studies performed on smaller manually annotated
datasets, and discovering new interesting reformulation patterns, including connections between reformulation types and topical
categories. We annotate two large query-flow graphs with reformulation type information, and run several graph-characterization
experiments on these graphs, extracting new insights about the relationships between the different query reformulation types.
Finally we study query recommendations based on short random walks on the query-flow graphs. Our experiments show that these
methods can match in precision, and often improve, recommendations based on query-click graphs, without the need of users’
clicks. Our experiments also show that it is important to consider transition-type labels on edges for having recommendations
of good quality. 相似文献