Semi-supervised document retrieval

This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSRank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSRank can effectively leverage the use of unlabeled data in learning.     

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.     

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.     

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.     

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR.     

Testing the cluster hypothesis in distributed information retrieval

How to merge and organise query results retrieved from different resources is one of the key issues in distributed information retrieval. Some previous research and experiments suggest that cluster-based document browsing is more effective than a single merged list. Cluster-based retrieval results presentation is based on the cluster hypothesis, which states that documents that cluster together have a similar relevance to a given query. However, while this hypothesis has been demonstrated to hold in classical information retrieval environments, it has never been fully tested in heterogeneous distributed information retrieval environments. Heterogeneous document representations, the presence of document duplicates, and disparate qualities of retrieval results, are major features of an heterogeneous distributed information retrieval environment that might disrupt the effectiveness of the cluster hypothesis. In this paper we report on an experimental investigation into the validity and effectiveness of the cluster hypothesis in highly heterogeneous distributed information retrieval environments. The results show that although clustering is affected by different retrieval results representations and quality, the cluster hypothesis still holds and that generating hierarchical clusters in highly heterogeneous distributed information retrieval environments is still a very effective way of presenting retrieval results to users.     

An agenda for green information retrieval research

Nowadays we use information retrieval systems and services as part of our many day-to-day activities ranging from a web and database search to searching for various digital libraries, audio and video collections/services, and so on. However, IR systems and services make extensive use of ICT (information and communication technologies) and increasing use of ICT can significantly increase greenhouse gas (GHG, a term used to denote emission of harmful gases in the atmosphere) emissions. Sustainable development, and more importantly environmental sustainability, has become a major area of concern of various national and international bodies and as a result various initiatives and measures are being proposed for reducing the environmental impact of industries, businesses, governments and institutions. Research also shows that appropriate use of ICT can reduce the overall GHG emissions of a business, product or service. Green IT and cloud computing can play a key role in reducing the environmental impact of ICT. This paper proposes the concept of Green IR systems and services that can play a key role in reducing the overall environmental impact of various ICT-based services in education and research, business, government, etc., that are increasingly being reliant on access and use of digital information. However, to date there has not been any systematic research towards building Green IR systems and services. This paper points out the major challenges in building Green IR systems and services, and two different methods are proposed for estimating the energy consumption, and the corresponding GHG emissions, of an IR system or service. This paper also proposes the four key enablers of a Green IR viz. Standardize, Share, Reuse and Green behavior. Further research required to achieve these for building Green IR systems and services are also mentioned.     

A reputation-based model for mobile agent migration for information search and retrieval

Mobile agent technology has been used in various applications including e-commerce, information processing, distributed network management, and database access. Information search and retrieval can be conducted by mobile agents in a decentralized system. As compared with the client/server model, the mobile agent approach has an advantage of saving network bandwidth and offering flexibility in information search and retrieval. In this paper, we present a model for mobile agents to select the most reputable information host to search and retrieve information. We use opinion-based belief structure to represent, aggregate and calculate the reputation of an information host. Since reputation is a multi-faced concept, our approach first allows the users to rank each information host's quality of service based on a set of evaluation categories. Then, a comprehensive, final reputation of the host is obtained by aggregating those specific category reputations. To recognize the subjective nature of a reputation, the transferable belief model is used to represent and rank the category reputation. Experiments are conducted using the Aglets technology to illustrate mobile agent migration.     

Document management systems from current capabilities towards intelligent information retrieval: an overview

Documents circulating in paper form are increasingly being substituted by its electronic equivalent in the modern office today so that any stored document can be retrieved whenever needed later on. The office worker is already burdened with information overload, so effective and efficient retrieval facilities become an important factor affecting worker productivity. This paper first reviews the features of current document management systems with varying facilities to manage, store and retrieve either reference to documents or whole documents. Information retrieval databases, groupware products and workflow management systems are presented as developments to handle different needs, together with the underlying concepts of knowledge management. The two problems of worker finiteness and worker ignorance remain outstanding, as they are only partially addressed by the above-mentioned systems. The solution lies in a shift away from pull technology where the user has to actively initiate the request for information towards push technology, where available information is automatically delivered without user intervention. Intelligent information retrieval agents are presented as a solution together with a marketing scenario of how they can be introduced.     

A lemmatization method for Mongolian and its application to indexing for information retrieval

In Mongolian, two different alphabets are used, Cyrillic and Mongolian. In this paper, we focus solely on the Mongolian language using the Cyrillic alphabet, in which a content word can be inflected when concatenated with one or more suffixes. Identifying the original form of content words is crucial for natural language processing and information retrieval. We propose a lemmatization method for Mongolian. The advantage of our lemmatization method is that it does not rely on noun dictionaries, enabling us to lemmatize out-of-dictionary words. We also apply our method to indexing for information retrieval. We use newspaper articles and technical abstracts in experiments that show the effectiveness of our method. Our research is the first significant exploration of the effectiveness of lemmatization for information retrieval in Mongolian.     

The effect of user characteristics on search effectiveness in information retrieval

This paper investigates the influence of user characteristics (e.g. search experience and cognitive skills) on user effectiveness. A user study was conducted to investigate this effect, 56 participants completed searches for 56 topics using the TREC test collection. Results indicated that participants with search experience and high cognitive skills were more effective than those with less experience and slower perceptual abilities. However, all users rated themselves with the same level of satisfaction with the search results despite the fact they varied substantially in their effectiveness. Therefore, information retrieval evaluators should take these factors into consideration when investigating the impact of system effectiveness on user effectiveness.     

Visualizing and mapping the intellectual structure of information retrieval

Information retrieval is a long established subfield of library and information science. Since its inception in the early- to mid -1950s, it has grown as a result, in part, of well-regarded retrieval system evaluation exercises/campaigns, the proliferation of Web search engines, and the expansion of digital libraries. Although researchers have examined the intellectual structure and nature of the general field of library and information science, the same cannot be said about the subfield of information retrieval. We address that in this work by sketching the information retrieval intellectual landscape through visualizations of citation behaviors. Citation data for 10 years (2000-2009) were retrieved from the Web of Science and analyzed using existing visualization techniques. Our results address information retrieval’s co-authorship network, highly productive authors, highly cited journals and papers, author-assigned keywords, active institutions, and the import of ideas from other disciplines.     

CLAIRE: A combinatorial visual analytics system for information retrieval evaluation

Information Retrieval (IR) develops complex systems, constituted of several components, which aim at returning and optimally ranking the most relevant documents in response to user queries. In this context, experimental evaluation plays a central role, since it allows for measuring IR systems effectiveness, increasing the understanding of their functioning, and better directing the efforts for improving them. Current evaluation methodologies are limited by two major factors: (i) IR systems are evaluated as “black boxes”, since it is not possible to decompose the contributions of the different components, e.g., stop lists, stemmers, and IR models; (ii) given that it is not possible to predict the effectiveness of an IR system, both academia and industry need to explore huge numbers of systems, originated by large combinatorial compositions of their components, to understand how they perform and how these components interact together.We propose a Combinatorial visuaL Analytics system for Information Retrieval Evaluation (CLAIRE) which allows for exploring and making sense of the performances of a large amount of IR systems, in order to quickly and intuitively grasp which system configurations are preferred, what are the contributions of the different components and how these components interact together.The CLAIRE system is then validated against use cases based on several test collections using a wide set of systems, generated by a combinatorial composition of several off-the-shelf components, representing the most common denominator almost always present in English IR systems. In particular, we validate the findings enabled by CLAIRE with respect to consolidated deep statistical analyses and we show that the CLAIRE system allows the generation of new insights, which were not detectable with traditional approaches.     

Automatic ranking of information retrieval systems using data fusion

Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the “(pseudo) relevant documents,” and employ these documents to evaluate and rank the systems. Experiments using Text REtrieval Conference (TREC) data provide statistically significant strong correlations with human-based assessments of the same systems. We hypothesize that the selection of systems that would return documents different from the majority could eliminate the ordinary systems from data fusion and provide better discrimination among the documents and systems. This could improve the effectiveness of automatic ranking. Based on this intuition, we introduce a new method for the selection of systems to be used for data fusion. For this purpose, we use the bias concept that measures the deviation of a system from the norm or majority and employ the systems with higher bias in the data fusion process. This approach provides even higher correlations with the human-based results. We demonstrate that our approach outperforms the previously proposed automatic ranking methods.     

Assigning appropriate weights for the linear combination data fusion method in information retrieval

In data fusion, the linear combination method is a very flexible method since different weights can be assigned to different systems. However, it remains an open question which weighting schema should be used. In some previous investigations and experiments, a simple weighting schema was used: for a system, its weight is assigned as its average performance over a group of training queries. However, it is not clear if this weighting schema is good or not. In some other investigations, different numerical optimisation methods were used to search for appropriate weights for the component systems. One major problem with those numerical optimisation methods is their low efficiency. It might not be feasible to use them in some situations, for example in some dynamic environments, system weights need to be updated from time to time for reasonably good performance. In this paper, we investigate the weighting issue by extensive experiments. The key point is to try to find the relation between performances of component systems and their corresponding weights which can lead to good fusion performance. We demonstrate that a series of power functions of average performance, which can be implemented as efficiently as the simple weighting schema, is more effective than the simple weighting schema for the linear data fusion method. Some other features of the power function weighting schema and the linear combination method are also investigated. The observations obtained from this study can be used directly in fusion applications of component retrieval results. The observations are also very useful for optimisation methods to choose better starting points and therefore to obtain more effective weights more quickly.     

The weighted Condorcet fusion in information retrieval

The Condorcet fusion is a distinctive fusion method and was found useful in information retrieval. Two basic requirements for the Condorcet fusion to improve retrieval effectiveness are: (1) all component systems involved should be more or less equally effective; and (2) each information retrieval system should be developed independently and thus each component result is more or less equally different from the others. These two requirements may not be satisfied in many cases, then weighted Condorcet becomes a good option. However, how to assign weights for the weighted Condorcet has not been investigated.     

A decision-theoretic approach to the evaluation of information retrieval systems

Evaluation research on information retrieval (IR) systems has thus far been narrowly focused and disjointed. This research attempts to narrow the gap by providing a comprehensive and integrated multiple criteria decision-theoretic approach for the evaluation of IR systems. The approach, which is based on the Analytic Hierarchy Process (AHP), is illustrated in the context of a domain-specific IR system. The novelty of this approach lies in the focus on the user aspect and the application of decision-making theories in the IR field.     

A relevance feedback mechanism for cluster-based retrieval

Contextual document clustering is a novel approach which uses information theoretic measures to cluster semantically related documents bound together by an implicit set of concepts or themes of narrow specificity. It facilitates cluster-based retrieval by assessing the similarity between a query and the cluster themes’ probability distribution. In this paper, we assess a relevance feedback mechanism, based on query refinement, that modifies the query’s probability distribution using a small number of documents that have been judged relevant to the query. We demonstrate that by providing only one relevance judgment, a performance improvement of 33% was obtained.