The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Jamie Callan - One of the best experts on this subject based on the ideXlab platform.

  • Federated Search for heterogeneous environments
    2011
    Co-Authors: Jamie Callan, Jaime Arguello
    Abstract:

    In information retrieval, Federated Search is the problem of automatically Searching across multiple distributed collections or resources. It is typically decomposed into two subsequent steps: deciding which resources to Search ( resource selection) and deciding how to combine results from multiple resources into a single presentation (results merging). Federated Search occurs in different environments. This dissertation focuses on an environment that has not been deeply investigated in prior work. The growing heterogeneity of digital media and the broad range of user information needs that occur in today's world have given rise to a multitude of systems that specialize on a specific type of Search task. Examples include Search for news, images, video, local businesses, items for sale, and even social-media interactions. In the Web Search domain, these specialized systems are called verticals and one important task for the Web Search engine is the prediction and integration of relevant vertical content into the Web Search results. This is known as aggregated web Search and is the main focus on this dissertation. Providing a single-point of access to all these diverse systems requires Federated Search solutions that can support result-type and retrieval-algorithm heterogeneity. This type of heterogeneity violates major assumptions made by state-of-the-art resource selection and results merging methods. While existing resource selection methods derive predictive evidence exclusively from sampled resource content, the approaches proposed in this dissertation draw on machine learning as a means to easily integrate various different types of evidence. These include, for example, evidence derived from (sampled) vertical content, vertical query-traffic, click-through information, and properties of the query string. In order to operate in a heterogeneous environment, we focus on methods that can learn a vertical-specific relationship between features and relevance. We also present methods that reduce the need for human-produced training data. Existing results merging methods formulate the task as score normalization. In a more heterogeneous environment, however, combining results into a single presentation requires satisfying a number of layout constraints. The dissertation proposes a novel formulation of the task: block ranking. During block-ranking, the objective is to rank sequences of results that must appear grouped together (vertically or horizontally) in the final presentation. Based on this formulation, the dissertation proposes and empirically validates a cost-effective methodology for evaluating aggregated web Search results. Finally, it proposes the use of machine learning methods for the task of block-ranking.

  • An effective and efficient results merging strategy for multilingual information retrieval in Federated Search environments
    Information Retrieval, 2008
    Co-Authors: Luo Si, Suleyman Cetintas, Jamie Callan, Hao Yuan
    Abstract:

    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) (http://www.clef-campaign.org/) 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 queryspecific 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). [ABSTRACT FROM AUTHOR]

  • Full-text Federated Search of text-based digital libraries in peer-to-peer networks
    Information Retrieval, 2006
    Co-Authors: Jamie Callan
    Abstract:

    Peer-to-peer (P2P) networks integrate autonomous computing resources without requiring a central coordinating authority, which makes them a potentially robust and scalable model for providing Federated Search capability to large-scale networks of text-based digital libraries. However, peer-to-peer networks have so far provided very limited support for full-text Federated Search with relevance-based document ranking. This paper provides solutions to full-text Federated Search of text-based digital libraries in hierarchical peer-to-peer networks. Existing approaches to full-text Search are adapted and new methods are developed for the problems of resource representation, resource selection, and result merging according to the unique characteristics of hierarchical peer-to-peer networks. Experimental results demonstrate that the proposed approaches offer a better combination of accuracy and efficiency than more common alternatives for Federated Search of text-based digital libraries in peer-to-peer networks.

  • user modeling for full text Federated Search in peer to peer networks
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006
    Co-Authors: Jamie Callan
    Abstract:

    User modeling for information retrieval has mostly been studied to improve the effectiveness of information access in centralized repositories. In this paper we explore user modeling in the context of full-text Federated Search in peer-to-peer networks. Our approach models a user's persistent, long-term interests based on past queries, and uses the model to improve Search efficiency for future queries that represent interests similar to past queries. Our approach also enables queries representing a user's transient, ad-hoc interests to be automatically recognized so that Search for these queries can rely on a relatively large Search radius to avoid sacrificing effectiveness for efficiency. Experimental results demonstrate that our approach can significantly improve the efficiency of full-text Federated Search without degrading its accuracy. Furthermore, the proposed approach does not require a large amount of training data, and is robust to a range of parameter values.

  • the fedlemur project Federated Search in the real world
    Journal of the Association for Information Science and Technology, 2006
    Co-Authors: Thi Truong Avrahami, Lawrence Yau, Jamie Callan
    Abstract:

    Federated Search and distributed information retrieval systems provide a single user interface for Searching multiple full-text Search engines. They have been an active area of reSearch for more than a decade, but in spite of their success as a reSearch topic, they are still rare in operational environments. This article discusses a prototype Federated Search system developed for the U.S. government's FedStats Web portal, and the issues addressed in adapting reSearch solutions to this operational environment. A series of experiments explore how well prior reSearch results, parameter settings, and heuristics apply in the FedStats environment. The article concludes with a set of lessons learned from this technology transfer effort, including observations about Search engine quality in the “real world.” © 2006 Wiley Periodicals, Inc.

Djoerd Hiemstra - One of the best experts on this subject based on the ideXlab platform.

  • overview of the trec 2013 Federated web Search track
    Text REtrieval Conference, 2014
    Co-Authors: Thomas Demeester, Dolf Trieschnigg, Dong Nguyen, Djoerd Hiemstra
    Abstract:

    The TREC Federated Web Search track is intended to promote reSearch related to Federated Search in a realistic web setting, and hereto provides a large data collection gathered from a series of online Search engines. This overview paper discusses the results of the first edition of the track, FedWeb 2013. The focus was on basic challenges in Federated Search: (1) resource selection, and (2) results merging. After an overview of the provided data collection and the relevance judgments for the test topics, the participants’ individual approaches and results on both tasks are discussed. Promising reSearch directions and an outlook on the 2014 edition of the track are provided as well.

  • Searchresultfinder Federated Search made easy
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013
    Co-Authors: Dolf Trieschnigg, Kien Tjinkamjet, Djoerd Hiemstra
    Abstract:

    Building a Federated Search engine based on a large number existing web Search engines is a challenge: implementing the programming interface (API) for each Search engine is an exacting and time-consuming job. In this demonstration we present SearchResultFinder, a browser plugin which speeds up determining reusable XPaths for extracting Search result items from HTML Search result pages. Based on a single Search result page, the tool presents a ranked list of candidate extraction XPaths and allows highlighting to view the extraction result. An evaluation with 148 web Search engines shows that in 90% of the cases a correct XPath is suggested.

  • Federated Search in the wild the combined power of over a hundred Search engines
    Conference on Information and Knowledge Management, 2012
    Co-Authors: Dong Nguyen, Dolf Trieschnigg, Thomas Demeester, Djoerd Hiemstra
    Abstract:

    Federated Search has the potential of improving web Search: the user becomes less dependent on a single Search provider and parts of the deep web become available through a unified interface, leading to a wider variety in the retrieved Search results. However, a publicly available dataset for Federated Search reflecting an actual web environment has been absent. As a result, it has been difficult to assess whether proposed systems are suitable for the web setting. We introduce a new test collection containing the results from more than a hundred actual Search engines, ranging from large general web Search engines such as Google and Bing to small domain-specific engines. We discuss the design and analyze the effect of several sampling methods. For a set of test queries, we collected relevance judgements for the top 10 results of each Search engine. The dataset is publicly available and is useful for reSearchers interested in resource selection for web Search collections, result merging and size estimation of uncooperative resources.

Dolf Trieschnigg - One of the best experts on this subject based on the ideXlab platform.

  • overview of the trec 2013 Federated web Search track
    Text REtrieval Conference, 2014
    Co-Authors: Thomas Demeester, Dolf Trieschnigg, Dong Nguyen, Djoerd Hiemstra
    Abstract:

    The TREC Federated Web Search track is intended to promote reSearch related to Federated Search in a realistic web setting, and hereto provides a large data collection gathered from a series of online Search engines. This overview paper discusses the results of the first edition of the track, FedWeb 2013. The focus was on basic challenges in Federated Search: (1) resource selection, and (2) results merging. After an overview of the provided data collection and the relevance judgments for the test topics, the participants’ individual approaches and results on both tasks are discussed. Promising reSearch directions and an outlook on the 2014 edition of the track are provided as well.

  • Searchresultfinder Federated Search made easy
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013
    Co-Authors: Dolf Trieschnigg, Kien Tjinkamjet, Djoerd Hiemstra
    Abstract:

    Building a Federated Search engine based on a large number existing web Search engines is a challenge: implementing the programming interface (API) for each Search engine is an exacting and time-consuming job. In this demonstration we present SearchResultFinder, a browser plugin which speeds up determining reusable XPaths for extracting Search result items from HTML Search result pages. Based on a single Search result page, the tool presents a ranked list of candidate extraction XPaths and allows highlighting to view the extraction result. An evaluation with 148 web Search engines shows that in 90% of the cases a correct XPath is suggested.

  • Federated Search in the wild the combined power of over a hundred Search engines
    Conference on Information and Knowledge Management, 2012
    Co-Authors: Dong Nguyen, Dolf Trieschnigg, Thomas Demeester, Djoerd Hiemstra
    Abstract:

    Federated Search has the potential of improving web Search: the user becomes less dependent on a single Search provider and parts of the deep web become available through a unified interface, leading to a wider variety in the retrieved Search results. However, a publicly available dataset for Federated Search reflecting an actual web environment has been absent. As a result, it has been difficult to assess whether proposed systems are suitable for the web setting. We introduce a new test collection containing the results from more than a hundred actual Search engines, ranging from large general web Search engines such as Google and Bing to small domain-specific engines. We discuss the design and analyze the effect of several sampling methods. For a set of test queries, we collected relevance judgements for the top 10 results of each Search engine. The dataset is publicly available and is useful for reSearchers interested in resource selection for web Search collections, result merging and size estimation of uncooperative resources.

Thomas Demeester - One of the best experts on this subject based on the ideXlab platform.

  • overview of the trec 2013 Federated web Search track
    Text REtrieval Conference, 2014
    Co-Authors: Thomas Demeester, Dolf Trieschnigg, Dong Nguyen, Djoerd Hiemstra
    Abstract:

    The TREC Federated Web Search track is intended to promote reSearch related to Federated Search in a realistic web setting, and hereto provides a large data collection gathered from a series of online Search engines. This overview paper discusses the results of the first edition of the track, FedWeb 2013. The focus was on basic challenges in Federated Search: (1) resource selection, and (2) results merging. After an overview of the provided data collection and the relevance judgments for the test topics, the participants’ individual approaches and results on both tasks are discussed. Promising reSearch directions and an outlook on the 2014 edition of the track are provided as well.

  • Federated Search in the wild the combined power of over a hundred Search engines
    Conference on Information and Knowledge Management, 2012
    Co-Authors: Dong Nguyen, Dolf Trieschnigg, Thomas Demeester, Djoerd Hiemstra
    Abstract:

    Federated Search has the potential of improving web Search: the user becomes less dependent on a single Search provider and parts of the deep web become available through a unified interface, leading to a wider variety in the retrieved Search results. However, a publicly available dataset for Federated Search reflecting an actual web environment has been absent. As a result, it has been difficult to assess whether proposed systems are suitable for the web setting. We introduce a new test collection containing the results from more than a hundred actual Search engines, ranging from large general web Search engines such as Google and Bing to small domain-specific engines. We discuss the design and analyze the effect of several sampling methods. For a set of test queries, we collected relevance judgements for the top 10 results of each Search engine. The dataset is publicly available and is useful for reSearchers interested in resource selection for web Search collections, result merging and size estimation of uncooperative resources.

Fabio Crestani - One of the best experts on this subject based on the ideXlab platform.

  • opinions in Federated Search university of lugano at trec 2014 Federated web Search track
    Text REtrieval Conference, 2014
    Co-Authors: Anastasia Giachanou, Ilya Markov, Fabio Crestani
    Abstract:

    Abstract : This technical report presents the work carried out at the University of Lugano on TREC 2014 Federated Web Search track. The main motivation behind our approach is to provide better coverage of opinions that are present in Federated resources. On the resource selection and vertical selection steps, we apply opinion mining to select opinionated resources/ verticals given a user's query. We do this by combining relevance-based selection with lexicon-based opinion mining. On the results merging step, we diversify the final document ranking based on sentiment using the retrieval-interpolated diversification method.

  • A multi-collection latent topic model for Federated Search
    Information Retrieval, 2011
    Co-Authors: Mark Baillie, Mark Carman, Fabio Crestani
    Abstract:

    Collection selection is a crucial function, central to the effectiveness and efficiency of a Federated information retrieval system. A variety of solutions have been proposed for collection selection adapting proven techniques used in centralised retrieval. This paper defines a new approach to collection selection that models the topical distribution in each collection. We describe an extended version of latent Dirichlet allocation that uses a hierarchical hyperprior to enable the different topical distributions found in each collection to be modelled. Under the model, resources are ranked based on the topical relationship between query and collection. By modelling collections in a low dimensional topic space, we can implicitly smooth their term-based characterisation with appropriate terms from topically related samples, thereby dealing with the problem of missing vocabulary within the samples. An important advantage of adopting this hierarchical model over current approaches is that the model generalises well to unseen documents given small samples of each collection. The latent structure of each collection can therefore be estimated well despite imperfect information for each collection such as sampled documents obtained through query-based sampling. Experiments demonstrate that this new, fully integrated topical model is more robust than current state of the art collection selection algorithms.