Selective web information retrieval

This thesis proposes selective Web information retrieval, a framework formulated in terms of statistical decision theory, with the aim to apply an appropriate retrieval approach on a per-query basis. The main component of the framework is a decision mechanism that selects an appropriate retrieval approach on a per-query basis. The selection of a particular retrieval approach is based on the outcome of an experiment, which is performed before the final ranking of the retrieved documents. The experiment is a process that extracts features from a sample of the set of retrieved documents. This thesis investigates three broad types of experiments. The first one counts the occurrences of query terms in the retrieved documents, indicating the extent to which the query topic is covered in the document collection. The second type of experiments considers information from the distribution of retrieved documents in larger aggregates of related Web documents, such as whole Web sites, or directories within Web sites. The third type of experiments estimates the usefulness of the hyperlink structure among a sample of the set of retrieved Web documents. The proposed experiments are evaluated in the context of both informational and navigational search tasks with an optimal Bayesian decision mechanism, where it is assumed that relevance information exists. This thesis further investigates the implications of applying selective Web information retrieval in an operational setting, where the tuning of a decision mechanism is based on limited existing relevance information and the information retrieval system’s input is a stream of queries related to mixed informational and navigational search tasks. First, the experiments are evaluated using different training and testing query sets, as well as a mixture of different types of queries. Second, query sampling is introduced, in order to approximate the queries that a retrieval system receives, and to tune an ad-hoc decision mechanism with a broad set of automatically sampled queries

Learning to select for information retrieval

The effective ranking of documents in search engines is based on various document features, such as the frequency of the query terms in each document, the length, or the authoritativeness of each document. In order to obtain a better retrieval performance, instead of using a single or a few features, there is a growing trend to create a ranking function by applying a learning to rank technique on a large set of features. Learning to rank techniques aim to generate an effective document ranking function by combining a large number of document features. Different ranking functions can be generated by using different learning to rank techniques or on different document feature sets. While the generated ranking function may be uniformly applied to all queries, several studies have shown that different ranking functions favour different queries, and that the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each individual query. This thesis proposes Learning to Select (LTS), a novel framework that selectively applies an appropriate ranking function on a per-query basis, regardless of the given query's type and the number of candidate ranking functions. In the learning to select framework, the effectiveness of a ranking function for an unseen query is estimated from the available neighbouring training queries. The proposed framework employs a classification technique (e.g. k-nearest neighbour) to identify neighbouring training queries for an unseen query by using a query feature. In particular, a divergence measure (e.g. Jensen-Shannon), which determines the extent to which a document ranking function alters the scores of an initial ranking of documents for a given query, is proposed for use as a query feature. The ranking function which performs the best on the identified training query set is then chosen for the unseen query. The proposed framework is thoroughly evaluated on two different TREC retrieval tasks (namely, Web search and adhoc search tasks) and on two large standard LETOR feature sets, which contain as many as 64 document features, deriving conclusions concerning the key components of LTS, namely the query feature and the identification of neighbouring queries components. Two different types of experiments are conducted. The first one is to select an appropriate ranking function from a number of candidate ranking functions. The second one is to select multiple appropriate document features from a number of candidate document features, for building a ranking function. Experimental results show that our proposed LTS framework is effective in both selecting an appropriate ranking function and selecting multiple appropriate document features, on a per-query basis. In addition, the retrieval performance is further enhanced when increasing the number of candidates, suggesting the robustness of the learning to select framework. This thesis also demonstrates how the LTS framework can be deployed to other search applications. These applications include the selective integration of a query independent feature into a document weighting scheme (e.g. BM25), the selective estimation of the relative importance of different query aspects in a search diversification task (the goal of the task is to retrieve a ranked list of documents that provides a maximum coverage for a given query, while avoiding excessive redundancy), and the selective application of an appropriate resource for expanding and enriching a given query for document search within an enterprise. The effectiveness of the LTS framework is observed across these search applications, and on different collections, including a large scale Web collection that contains over 50 million documents. This suggests the generality of the proposed learning to select framework. The main contributions of this thesis are the introduction of the LTS framework and the proposed use of divergence measures as query features for identifying similar queries. In addition, this thesis draws insights from a large set of experiments, involving four different standard collections, four different search tasks and large document feature sets. This illustrates the effectiveness, robustness and generality of the LTS framework in tackling various retrieval applications

Automatic cataloguing of Web resources on a personalized taxonomy

Information overload is a major concerns retrieval systems face. Information is ubiquitous, available from many distinct sources and the main issue is to get just the right piece of information that might satisfy our specific needs. Many of these sources organize their informational resources on a given ontology. However, these ontologies are static and do not allow for personalization. This fact degrades the value of the service if there is no easy mental mapping between user specific needs and the general source ontology. Organizing informational resources according to particular needs might increase users’ satisfaction and save their time. In this paper we present a methodology to filter and organize informational resources according to users’ interests, thus granting users with a personalized edition of the resource, especially tailored towards their specific needs. We believe that this methodology may be applied in educational scenarios, where we have a repository of educational objects that are organized according to specific objectives, automatically producing specific courseware. Our experimental results confirm that it is possible to automatically personalize document resources with high precision at a reduced editor workload.This work is supported by the POSC/EIA/58367/2004/Site-o-Matic Project (Fundação Ciência e Tecnologia), FEDER e Programa de Financiamento Plurianual de Unidades de I & D. We would like to thank the Expresso newspaper for their support throughout this work.info:eu-repo/semantics/publishedVersio

The Present and Future of Internet Search

Search engines were crucial in the development of the World Wide Web. Web-based information retrieval progressed from simple word matching to sophisticated algorithms for maximizing the relevance of search results. Statistical and graph-based approaches for indexing and ranking pages, natural language processing techniques for improving query results, and intelligent agents for personalizing the search process all show great promise for enhanced performance. The evolution in search technology was accompanied by growing economic pressures on search engine companies. Unable to sustain long-term viability from advertising revenues, many of the original search engines diversified into portals that farm out their search and directory operations. Vertical portals that serve focused user communities also outsource their search services, and even directory providers began to integrate search engine technologies from outside vendors. This article brings order to the chaos resulting from the variety of search tools being offered under various marketing guises. While growing reliance on a small set of search providers is leading to less diversity among search services, users can expect individualized searching experiences that factor in personal information. The convergence of technology and business models also results in more narrowly defined search spaces, which will lessen the quantity of search results while improving their quality

The voting model for people search

The thesis investigates how persons in an enterprise organisation can be ranked in response to a query, so that those persons with relevant expertise to the query topic are ranked first. The expertise areas of the persons are represented by documentary evidence of expertise, known as candidate profiles. The statement of this research work is that the expert search task in an enterprise setting can be successfully and effectively modelled using a voting paradigm. In the so-called Voting Model, when a document is retrieved for a query, this document represents a vote for every expert associated with the document to have relevant expertise to the query topic. This voting paradigm is manifested by the proposition of various voting techniques that aggregate the votes from documents to candidate experts. Moreover, the research work demonstrates that these voting techniques can be modelled in terms of a Bayesian belief network, providing probabilistic semantics for the proposed voting paradigm. The proposed voting techniques are thoroughly evaluated on three standard expert search test collections, deriving conclusions concerning each component of the Voting Model, namely the method used to identify the documents that represent each candidate's expertise areas, the weighting models that are used to rank the documents, and the voting techniques which are used to convert the ranking of documents into the ranking of experts. Effective settings are identified and insights about the behaviour of each voting technique are derived. Moreover, the practical aspects of deploying an expert search engine such as its efficiency and how it should be trained are also discussed. This thesis includes an investigation of the relationship between the quality of the underlying ranking of documents and the resulting effectiveness of the voting techniques. The thesis shows that various effective document retrieval approaches have a positive impact on the performance of the voting techniques. Interestingly, it also shows that a `perfect' ranking of documents does not necessarily translate into an equally perfect ranking of candidates. Insights are provided into the reasons for this, which relate to the complexity of evaluating tasks based on ranking aggregates of documents. Furthermore, it is shown how query expansion can be adapted and integrated into the expert search process, such that the query expansion successfully acts on a pseudo-relevant set containing only a list of names of persons. Five ways of performing query expansion in the expert search task are proposed, which vary in the extent to which they tackle expert search-specific problems, in particular, the occurrence of topic drift within the expertise evidence for each candidate. Not all documentary evidence of expertise for a given person are equally useful, nor may there be sufficient expertise evidence for a relevant person within an enterprise. This thesis investigates various approaches to identify the high quality evidence for each person, and shows how the World Wide Web can be mined as a resource to find additional expertise evidence. This thesis also demonstrates how the proposed model can be applied to other people search tasks such as ranking blog(ger)s in the blogosphere setting, and suggesting reviewers for the submitted papers to an academic conference. The central contributions of this thesis are the introduction of the Voting Model, and the definition of a number of voting techniques within the model. The thesis draws insights from an extremely large and exhaustive set of experiments, involving many experimental parameters, and using different test collections for several people search tasks. This illustrates the effectiveness and the generality of the Voting Model at tackling various people search tasks and, indeed, the retrieval of aggregates of documents in general

Social impact retrieval: measuring author influence on information retrieval

The increased presence of technologies collectively referred to as Web 2.0 mean the entire process of new media production and dissemination has moved away from an authorcentric approach. Casual web users and browsers are increasingly able to play a more active role in the information creation process. This means that the traditional ways in which information sources may be validated and scored must adapt accordingly. In this thesis we propose a new way in which to look at a user's contributions to the network in which they are present, using these interactions to provide a measure of authority and centrality to the user. This measure is then used to attribute an query-independent interest score to each of the contributions the author makes, enabling us to provide other users with relevant information which has been of greatest interest to a community of like-minded users. This is done through the development of two algorithms; AuthorRank and MessageRank. We present two real-world user experiments which focussed around multimedia annotation and browsing systems that we built; these systems were novel in themselves, bringing together video and text browsing, as well as free-text annotation. Using these systems as examples of real-world applications for our approaches, we then look at a larger-scale experiment based on the author and citation networks of a ten year period of the ACM SIGIR conference on information retrieval between 1997-2007. We use the citation context of SIGIR publications as a proxy for annotations, constructing large social networks between authors. Against these networks we show the effectiveness of incorporating user generated content, or annotations, to improve information retrieval

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Learning object metadata surrogates in search result interfaces: user evaluation, design and content

The purpose of this research was to evaluate user interaction with learning object metadata surrogates both in terms of content and presentation. The main objectives of this study were: (1) to review the literature on learning object metadata and user-centred evaluation of metadata surrogates in the context of cognitive information retrieval (including user-centred relevance and usability research); (2) to develop a framework for the evaluation of user interaction with learning object metadata surrogates in search result interfaces; (3) to investigate the usability of metadata surrogates in search result interfaces of learning object repositories (LORs) in terms of various presentation aspects (such as amount of information, structure and highlighting of query terms) as a means for facilitating the user relevance judgment process; (4) to investigate in-depth the type of content that should be included in learning object metadata surrogates in order to facilitate the process of relevance judgment; (5) to provide a set of recommendations—guidelines for the design of learning object metadata surrogates in search result interfaces both in terms of content and presentation. [Continues.

Index ordering by query-independent measures

There is an ever-increasing amount of data that is being produced from various data sources — this data must then be organised effectively if we hope to search though it. Traditional information retrieval approaches search through all available data in a particular collection in order to find the most suitable results, however, for particularly large collections this may be extremely time consuming. Our purposed solution to this problem is to only search a limited amount of the collection at query-time, in order to speed this retrieval process up. Although, in doing this we aim to limit the loss in retrieval efficacy (in terms of accuracy of results). The way we aim to do this is to firstly identify the most “important” documents within the collection, and then sort the documents within the collection in order of their "importance” in the collection. In this way we can choose to limit the amount of information to search through, by eliminating the documents of lesser importance, which should not only make the search more efficient, but should also limit any loss in retrieval accuracy. In this thesis we investigate various different query-independent methods that may indicate the importance of a document in a collection. The more accurate the measure is at determining an important document, the more effectively we can eliminate documents from the retrieval process - improving the query-throughput of the system, as well as providing a high level of accuracy in the returned results. The effectiveness of these approaches are evaluated using the datasets provided by the terabyte track at the Text REtreival Conference (TREC)