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1.
In this paper the problem of indexing heterogeneous structured documents and of retrieving semi-structured documents is considered. We propose a flexible paradigm for both indexing such documents and formulating user queries specifying soft constraints on both documents structure and content. At the indexing level we propose a model that achieves flexibility by constructing personalised document representations based on users views of the documents. This is obtained by allowing users to specify their preferences on the documents sections that they estimate to bear the most interesting information, as well as to linguistically quantify the number of sections which determine the global potential interest of the documents. At the query language level, a flexible query language for expressing soft selection conditions on both the documents structure and content is proposed. 相似文献
2.
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
3.
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
4.
Fernando Martínez-Santiago L. Alfonso Ureña-López Maite Martín-Valdivia 《Information Retrieval》2006,9(1):71-93
A usual strategy to implement CLIR (Cross-Language Information Retrieval) systems is the so-called query translation approach.
The user query is translated for each language present in the multilingual collection in order to compute an independent monolingual
information retrieval process per language. Thus, this approach divides documents according to language. In this way, we obtain
as many different collections as languages. After searching in these corpora and obtaining a result list per language, we
must merge them in order to provide a single list of retrieved articles.
In this paper, we propose an approach to obtain a single list of relevant documents for CLIR systems driven by query translation.
This approach, which we call 2-step RSV (RSV: Retrieval Status Value), is based on the re-indexing of the retrieval documents
according to the query vocabulary, and it performs noticeably better than traditional methods.
The proposed method requires query vocabulary alignment: given a word for a given query, we must know the translation or translations
to the other languages. Because this is not always possible, we have researched on a mixed model. This mixed model is applied
in order to deal with queries with partial word-level alignment. The results prove that even in this scenario, 2-step RSV
performs better than traditional merging methods. 相似文献
5.
Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information. 相似文献
6.
Search engine results are often biased towards a certain aspect of a query or towards a certain meaning for ambiguous query terms. Diversification of search results offers a way to supply the user with a better balanced result set increasing the probability that a user finds at least one document suiting her information need. In this paper, we present a reranking approach based on minimizing variance of Web search results to improve topic coverage in the top-k results. We investigate two different document representations as the basis for reranking. Smoothed language models and topic models derived by Latent Dirichlet?allocation. To evaluate our approach we selected 240 queries from Wikipedia disambiguation pages. This provides us with ambiguous queries together with a community generated balanced representation of their (sub)topics. For these queries we crawled two major commercial search engines. In addition, we present a new evaluation strategy based on Kullback-Leibler divergence and Wikipedia. We evaluate this method using the TREC sub-topic evaluation on the one hand, and manually annotated query results on the other hand. Our results show that minimizing variance in search results by reranking relevant pages significantly improves topic coverage in the top-k results with respect to Wikipedia, and gives a good overview of the overall search result. Moreover, latent topic models achieve competitive diversification with significantly less reranking. Finally, our evaluation reveals that our automatic evaluation strategy using Kullback-Leibler divergence correlates well with α-nDCG scores used in manual evaluation efforts. 相似文献
7.
Multilingual information retrieval is generally understood to mean the retrieval of relevant information in multiple target
languages in response to a user query in a single source language. In a multilingual federated search environment, different
information sources contain documents in different languages. A general search strategy in multilingual federated search environments
is to translate the user query to each language of the information sources and run a monolingual search in each information
source. It is then necessary to obtain a single ranked document list by merging the individual ranked lists from the information
sources that are in different languages. This is known as the results merging problem for multilingual information retrieval.
Previous research has shown that the simple approach of normalizing source-specific document scores is not effective. On the
other side, a more effective merging method was proposed to download and translate all retrieved documents into the source
language and generate the final ranked list by running a monolingual search in the search client. The latter method is more
effective but is associated with a large amount of online communication and computation costs. This paper proposes an effective
and efficient approach for the results merging task of multilingual ranked lists. Particularly, it downloads only a small
number of documents from the individual ranked lists of each user query to calculate comparable document scores by utilizing
both the query-based translation method and the document-based translation method. Then, query-specific and source-specific
transformation models can be trained for individual ranked lists by using the information of these downloaded documents. These
transformation models are used to estimate comparable document scores for all retrieved documents and thus the documents can
be sorted into a final ranked list. This merging approach is efficient as only a subset of the retrieved documents are downloaded
and translated online. Furthermore, an extensive set of experiments on the Cross-Language Evaluation Forum (CLEF) () data has demonstrated the effectiveness of the query-specific and source-specific results merging algorithm against other
alternatives. The new research in this paper proposes different variants of the query-specific and source-specific results
merging algorithm with different transformation models. This paper also provides thorough experimental results as well as
detailed analysis. All of the work substantially extends the preliminary research in (Si and Callan, in: Peters (ed.) Results
of the cross-language evaluation forum-CLEF 2005, 2005).
相似文献
| Hao YuanEmail: |
8.
An information retrieval (IR) system can often fail to retrieve relevant documents due to the incomplete specification of information need in the user’s query. Pseudo-relevance feedback (PRF) aims to improve IR effectiveness by exploiting potentially relevant aspects of the information need present in the documents retrieved in an initial search. Standard PRF approaches utilize the information contained in these top ranked documents from the initial search with the assumption that documents as a whole are relevant to the information need. However, in practice, documents are often multi-topical where only a portion of the documents may be relevant to the query. In this situation, exploitation of the topical composition of the top ranked documents, estimated with statistical topic modeling based approaches, can potentially be a useful cue to improve PRF effectiveness. The key idea behind our PRF method is to use the term-topic and the document-topic distributions obtained from topic modeling over the set of top ranked documents to re-rank the initially retrieved documents. The objective is to improve the ranks of documents that are primarily composed of the relevant topics expressed in the information need of the query. Our RF model can further be improved by making use of non-parametric topic modeling, where the number of topics can grow according to the document contents, thus giving the RF model the capability to adjust the number of topics based on the content of the top ranked documents. We empirically validate our topic model based RF approach on two document collections of diverse length and topical composition characteristics: (1) ad-hoc retrieval using the TREC 6-8 and the TREC Robust ’04 dataset, and (2) tweet retrieval using the TREC Microblog ’11 dataset. Results indicate that our proposed approach increases MAP by up to 9% in comparison to the results obtained with an LDA based language model (for initial retrieval) coupled with the relevance model (for feedback). Moreover, the non-parametric version of our proposed approach is shown to be more effective than its parametric counterpart due to its advantage of adapting the number of topics, improving results by up to 5.6% of MAP compared to the parametric version. 相似文献
9.
10.
Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections. 相似文献
11.
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
12.
交互式跨语言信息检索是信息检索的一个重要分支。在分析交互式跨语言信息检索过程、评价指标、用户行为进展等理论研究基础上,设计一个让用户参与跨语言信息检索全过程的用户检索实验。实验结果表明:用户检索词主要来自检索主题的标题;用户判断文档相关性的准确率较高;目标语言文档全文、译文摘要、译文全文都是用户认可的判断依据;翻译优化方法以及翻译优化与查询扩展的结合方法在用户交互环境下非常有效;用户对于反馈后的翻译仍然愿意做进一步选择;用户对于与跨语言信息检索系统进行交互是有需求并认可的。用户行为分析有助于指导交互式跨语言信息检索系统的设计与实践。 相似文献
13.
王以群 《现代图书情报技术》1996,12(4):48-50
联机检索过程中存在三个概念: 提问概念、标引概念、检索概念。本文从人为因素、检索语言、词表结构三个方面, 讨论及其对上述三个概念匹配的影响, 提出研究与发展混合型检索语言、确定检索人员新的专业标准等减少概念匹配失误的设想。 相似文献
14.
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
15.
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
16.
Robert W. P. Luk 《Information Retrieval》2008,11(6):539-561
This paper discusses various issues about the rank equivalence of Lafferty and Zhai between the log-odds ratio and the query
likelihood of probabilistic retrieval models. It highlights that Robertson’s concerns about this equivalence may arise when
multiple probability distributions are assumed to be uniformly distributed, after assuming that the marginal probability logically
follows from Kolmogorov’s probability axioms. It also clarifies that there are two types of rank equivalence relations between
probabilistic models, namely strict and weak rank equivalence. This paper focuses on the strict rank equivalence which requires
the event spaces of the participating probabilistic models to be identical. It is possible that two probabilistic models are
strict rank equivalent when they use different probability estimation methods. This paper shows that the query likelihood,
p(q|d, r), is strict rank equivalent to p(q|d) of the language model of Ponte and Croft by applying assumptions 1 and 2 of Lafferty and Zhai. In addition, some statistical
component language model may be strict rank equivalent to the log-odds ratio, and that some statistical component model using
the log-odds ratio may be strict rank equivalent to the query likelihood. Finally, we suggest adding a random variable for
the user information need to the probabilistic retrieval models for clarification when these models deal with multiple requests. 相似文献
17.
Coordinated by the U.S. Geological Survey (USGS), the National Biological Information Infrastructure (NBII) is a Web-based system that provides access to data and information on the nation’s biological resources. Although it was begun in 1993, predating any formal E-Government initiative, the NBII typifies the E-Government concepts outlined in the President’s Management Agenda, as well as in the proposed E-Government Act of 2002. This article—an individual case study and not a broad survey with extensive references to the literature—explores the structure and operation of the NBII in relation to several emerging trends in E-Government: end-user focus, defined and scalable milestones, public-private partnerships, alliances with stakeholders, and interagency cooperation. 相似文献
18.
The number of Web users whose first language is not English continues to grow, as does the amount of content provided in languages other than English. This poses new challenges for actors on the Web, such as in which language(s) content should be offered, how search tools should deal with mono- and multilingual content, and how users can make the best use of navigation and search options, suited to their individual linguistic skills. How should these challenges be dealt with? Technological approaches to non-English (or in general, cross-language) Web search have made large progress; however, translation remains a hard problem. This precludes a low-cost but high-quality blanket all-language coverage of the whole Web. In this paper, we propose a user-centric approach to answering questions of where to best concentrate efforts and investments. Drawing on linguistic research, we describe data on the availability of content and access to it in first and second languages across the Web. We then present three studies that investigated the impact of the availability (or not) of first-language content and access forms on user behaviour and attitudes. The results indicate that non-English languages are under-represented on the Web and that this is partly due to content-creation, link-setting and link-following behaviour. They also show that user satisfaction is influenced both by the cognitive effort of searching and the availability of alternative information in that language. These findings suggest that more cross-language tools are desirable. However, they also indicate that context (such as user groups’ domain expertise or site type) should be considered when tradeoffs between information quality and multilinguality need to be taken into account. 相似文献
19.
Large-scale retrieval systems are often implemented as a cascading sequence of phases—a first filtering step, in which a large set of candidate documents are extracted using a simple technique such as Boolean matching and/or static document scores; and then one or more ranking steps, in which the pool of documents retrieved by the filter is scored more precisely using dozens or perhaps hundreds of different features. The documents returned to the user are then taken from the head of the final ranked list. Here we examine methods for measuring the quality of filtering and preliminary ranking stages, and show how to use these measurements to tune the overall performance of the system. Standard top-weighted metrics used for overall system evaluation are not appropriate for assessing filtering stages, since the output is a set of documents, rather than an ordered sequence of documents. Instead, we use an approach in which a quality score is computed based on the discrepancy between filtered and full evaluation. Unlike previous approaches, our methods do not require relevance judgments, and thus can be used with virtually any query set. We show that this quality score directly correlates with actual differences in measured effectiveness when relevance judgments are available. Since the quality score does not require relevance judgments, it can be used to identify queries that perform particularly poorly for a given filter. Using these methods, we explore a wide range of filtering options using thousands of queries, categorize the relative merits of the different approaches, and identify useful parameter combinations. 相似文献
20.
《Microprocessing and Microprogramming》1994,40(10-12):905-908
The query language OVAL which is intended for the integration with the database programming language based on C++ is proposed in this paper. The work addresses the impedance mismatch problem [1] between the syntax and the semantics of the programming and query language. The query language OVAL is based on the functional query language FQL [3] extending it for the manipulation of complex objects. The salient features of the OVAL query language are: (i) functional nature of the query language, which makes the language suitable for the integration with the procedural programming languages and provides modular style of query definition, (ii) the use of schema information for expressing queries and (iii) recursive evaluation of the algebraic operations on set structured complex objects. 相似文献