Using query logs to establish vocabularies in distributed information retrieval

Users of search engines express their needs as queries, typically consisting of a small number of terms. The resulting search engine query logs are valuable resources that can be used to predict how people interact with the search system. In this paper, we introduce two novel applications of query logs, in the context of distributed information retrieval. First, we use query log terms to guide sampling from uncooperative distributed collections. We show that while our sampling strategy is at least as efficient as current methods, it consistently performs better. Second, we propose and evaluate a pruning strategy that uses query log information to eliminate terms. Our experiments show that our proposed pruning method maintains the accuracy achieved by complete indexes, while decreasing the index size by up to 60%. While such pruning may not always be desirable in practice, it provides a useful benchmark against which other pruning strategies can be measured.     

Performance analysis of distributed information retrieval architectures using an improved network simulation model

The increasing number of documents that have to be indexed in different environments, particularly on the Web, and the lack of scalability of a single centralised index lead to the use of distributed information retrieval systems to effectively search for and locate the required information. In this study, we present several improvements over the two main bottlenecks in a distributed information retrieval system (the network and the brokers). We extend a simulation network model in order to represent a switched network. The new simulation model is validated by comparing the estimated response times with those obtained using a real system. We show that the use of a switched network reduces the saturation of the interconnection network, especially in a replicated system, and some improvements may be achieved using multicast messages and faster connections with the brokers. We also demonstrate that reducing the partial results sets will improve the response time of a distributed system by 53%, with a negligible probability of changing the system’s precision and recall values. Finally, we present a simple hierarchical distributed broker model that will reduce the response times for a distributed system by 55%.     

A results merging algorithm for distributed information retrieval environments that combines regression methodologies with a selective download phase

The problem of results merging in distributed information retrieval environments has gained significant attention the last years. Two generic approaches have been introduced in research. The first approach aims at estimating the relevance of the documents returned from the remote collections through ad hoc methodologies (such as weighted score merging, regression etc.) while the other is based on downloading all the documents locally, completely or partially, in order to calculate their relevance. Both approaches have advantages and disadvantages. Download methodologies are more effective but they pose a significant overhead on the process in terms of time and bandwidth. Approaches that rely solely on estimation on the other hand, usually depend on document relevance scores being reported by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging algorithms, need a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced is based on adaptively downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. Thus it reconciles the above two approaches, combining their strengths, while minimizing their drawbacks, achieving the limited time and bandwidth overhead of the estimation approaches and the increased effectiveness of the download. The proposed algorithm is tested in a variety of settings and its performance is found to be significantly better than the former, while approximating that of the latter.     

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques.     

The Dilution/Concentration conditions for cross-language information retrieval models

Experimental results of cross-language information retrieval (CLIR) do not indicate why a model fails or how a model could be improved. One basic research question is thus whether it is possible to provide conditions by which one can evaluate any existing or new CLIR strategy analytically and one can improve the design of CLIR models. Inspired by the heuristics in monolingual IR, we introduce in this paper Dilution/Concentration (D/C) conditions to characterize good CLIR models based on direct intuitions under artificial settings. The conditions, derived from first principles in CLIR, generalize the idea of query structuring approach. Empirical results with state-of-the-art CLIR models show that when a condition is not satisfied, it often indicates non-optimality of the method. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies the conditions. Lastly, we propose, by following the D/C conditions, several novel CLIR models based on the information-based models, which again shows that the D/C conditions are efficient to feature good CLIR models.     

Using information gain to improve multi-modal information retrieval systems

Nowadays, access to information requires managing multimedia databases effectively, and so, multi-modal retrieval techniques (particularly images retrieval) have become an active research direction. In the past few years, a lot of content-based image retrieval (CBIR) systems have been developed. However, despite the progress achieved in the CBIR, the retrieval accuracy of current systems is still limited and often worse than only textual information retrieval systems. In this paper, we propose to combine content-based and text-based approaches to multi-modal retrieval in order to achieve better results and overcome the lacks of these techniques when they are taken separately. For this purpose, we use a medical collection that includes both images and non-structured text. We retrieve images from a CBIR system and textual information through a traditional information retrieval system. Then, we combine the results obtained from both systems in order to improve the final performance. Furthermore, we use the information gain (IG) measure to reduce and improve the textual information included in multi-modal information retrieval systems. We have carried out several experiments that combine this reduction technique with a visual and textual information merger. The results obtained are highly promising and show the profit obtained when textual information is managed to improve conventional multi-modal systems.     

A probabilistic model of information retrieval: development and comparative experiments: Part 2

The paper combines a comprehensive account of the probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects of retrieval data and a wider range of system functions. Each step in the argument is matched by comparative retrieval tests, to provide a single coherent account of a major line of research. The experiments demonstrate, for a large test collection, that the probabilistic model is effective and robust, and that it responds appropriately, with major improvements in performance, to key features of retrieval situations.Part 1 covers the foundations and the model development for document collection and relevance data, along with the test apparatus. Part 2 covers the further development and elaboration of the model, with extensive testing, and briefly considers other environment conditions and tasks, model training, concluding with comparisons with other approaches and an overall assessment.Data and results tables for both parts are given in Part 1. Key results are summarised in Part 2.     

Uncertainty in information seeking and retrieval: A study in an academic environment

This research focuses specifically on uncertainty and information seeking in a digital environment. In this research we argue that different types of uncertainty are associated with the information seeking process and that, with the proliferation of new and different search tools, sources and channels, uncertainty, positive/desirable or negative/undesirable, continues to be a significant factor in the search process. Users may feel uncertain at any stage of the information search and retrieval process and uncertainty may remain even after completion of the process resulting in what may be called persistent uncertainty. An online questionnaire was used to collect data from users in the higher education sector. There were three parts to the questionnaire focusing on: information seeking activities, information seeking problems, and access to specific information channels or sources. Quantitative analysis was carried out on the data collected through the online questionnaire. A total of 668 responses were returned from the chosen user categories of academic staff, research staff and research students. This research has shown that there are some information seeking activities and information seeking problems that are the most common causes of uncertainty among significant number of users from different disciplines, age, gender, ICT skills, etc. This is also the case with respect to access to and use of specific information sources/channels, although the degrees of uncertainty in relation are relatively small. Possible implications of this study and further research issues are indicated.     

Ontology refinement for improved information retrieval

Ontologies are frequently used in information retrieval being their main applications the expansion of queries, semantic indexing of documents and the organization of search results. Ontologies provide lexical items, allow conceptual normalization and provide different types of relations. However, the optimization of an ontology to perform information retrieval tasks is still unclear. In this paper, we use an ontology query model to analyze the usefulness of ontologies in effectively performing document searches. Moreover, we propose an algorithm to refine ontologies for information retrieval tasks with preliminary positive results.     

Bipolar queries in textual information retrieval: A new perspective

A new concept of a bipolar query against collections of textual documents, i.e. in the context of information retrieval (IR), is introduced using recent developments in bipolar information modeling and bipolar database queries. Specifically, a particular approach to bipolar queries with an explicit “and possibly” type of an aggregation operator is used. An effective and efficient processing of such bipolar queries using standard IR data structures is briefly discussed. The bipolar queries proposed combine a flexibility provided by fuzzy logic with a more sophisticated representation of user preferences and intentions. This combination can make the search of vast resources of textual document, notably those available via the Internet, more intelligent.     

Evaluating subtopic retrieval methods: Clustering versus diversification of search results

To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels.     

Specifics of information retrieval for young users: A survey

In this paper, we present the state of the art in the field of information retrieval that is relevant for understanding how to design information retrieval systems for children. We describe basic theories of human development to explain the specifics of young users, i.e., their cognitive skills, fine motor skills, knowledge, memory and emotional states in so far as they differ from those of adults. We derive the implications these differences have on the design of information retrieval systems for children. Furthermore, we summarize the main findings about children’s search behavior from multiple user studies. These findings are important to understand children’s information needs, their search strategies and usage of information retrieval systems. We also identify several weaknesses of previous user studies about children’s information-seeking behavior. Guided by the findings of these user studies, we describe challenges for the design of information retrieval systems for young users. We give an overview of algorithms and user interface concepts. We also describe existing information retrieval systems for children, in specific web search engines and digital libraries. We conclude with a discussion of open issues and directions for further research. The survey provided in this paper is important both for designers of information retrieval systems for young users as well as for researchers who start working in this field.     

医学情报检索课教学探讨

开设《情检》课,是高等医药院校、教学改革的需要,也是高校图书馆发挥教育职能和情报职能的重要手段。本文根据我馆十几年的教学实践,谈一点体会。     

Global term weights in distributed environments

This paper examines the estimation of global term weights (such as IDF) in information retrieval scenarios where a global view on the collection is not available. In particular, the two options of either sampling documents or of using a reference corpus independent of the target retrieval collection are compared using standard IR test collections. In addition, the possibility of pruning term lists based on frequency is evaluated.     

Relevance feedback and cross-language information retrieval

This paper presents a study of relevance feedback in a cross-language information retrieval environment. We have performed an experiment in which Portuguese speakers are asked to judge the relevance of English documents; documents hand-translated to Portuguese and documents automatically translated to Portuguese. The goals of the experiment were to answer two questions (i) how well can native Portuguese searchers recognise relevant documents written in English, compared to documents that are hand translated and automatically translated to Portuguese; and (ii) what is the impact of misjudged documents on the performance improvement that can be achieved by relevance feedback. Surprisingly, the results show that machine translation is as effective as hand translation in aiding users to assess relevance in the experiment. In addition, the impact of misjudged documents on the performance of RF is overall just moderate, and varies greatly for different query topics.     

Engineering a multi-purpose test collection for Web retrieval experiments

Past research into text retrieval methods for the Web has been restricted by the lack of a test collection capable of supporting experiments which are both realistic and reproducible. The 1.69 million document WT10g collection is proposed as a multi-purpose testbed for experiments with these attributes, in distributed IR, hyperlink algorithms and conventional ad hoc retrieval.WT10g was constructed by selecting from a superset of documents in such a way that desirable corpus properties were preserved or optimised. These properties include: a high degree of inter-server connectivity, integrity of server holdings, inclusion of documents related to a very wide spread of likely queries, and a realistic distribution of server holding sizes. We confirm that WT10g contains exploitable link information using a site (homepage) finding experiment. Our results show that, on this task, Okapi BM25 works better on propagated link anchor text than on full text.WT10g was used in TREC-9 and TREC-2000 and both topic relevance and homepage finding queries and judgments are available.     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.     

高校图书馆信息检索教学的改进策略

随着信息技术的快速发展,网络资源已经成为人们获取信息的最主要渠道,特别是在高校教学中,如何提高大学生科学获取信息的能力,是提高高校教育教学质量的重要的组成部分,本文结合当今高校图书馆在文献检索教学中存在的一些问题及改进的策略进行了分析,并提出了一些改进的措施。