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Weiyi Meng - One of the best experts on this subject based on the ideXlab platform.

  • Original article Rule-based deduplication of article records from bibliographic databases
    2016
    Co-Authors: Yu Jiang, Can Lin, Weiyi Meng, Aaron M. Cohen, Neil R Smalheiser
    Abstract:

    Vol. 2014: article ID bat086; doi:10.1093/database/bat086. We recently designed and deployed a Metasearch Engine, Metta, that sends queries and retrieves search results from fiv

  • RESEARCH Open Access Design and implementation of Metta, a
    2016
    Co-Authors: Marian S Mcdonagh, Weiyi Meng
    Abstract:

    Metasearch Engine for biomedical literature retrieval intended for systematic reviewer

  • and
    2015
    Co-Authors: Weiyi Meng, Kinglup Liu, Name Weiyi Meng, Name King-lup Liu
    Abstract:

    Frequently a user's information needs are stored in the databases of multiple search Engines. It is inconvenient and ineÆcient for an ordinary user to invoke multiple search Engines and identify useful documents from the returned results. To support unied access to multiple search Engines, a Metasearch Engine can be constructed. When a Metasearch Engine receives a query from a user, it invokes the underlying search Engines to retrieve useful information for the user. Metasearch Engines have other benets as a search tool such as increasing the search coverage of the Web and improving the scalability of the search. In this article, we survey techniques that have been proposed to tackle several underlying challenges for building a good Metasearch Engine. Among the main challenges, the database selection problem is to identify search Engines that are likely to return useful documents to a given query. The document selection problem is to determine what documents to retrieve from each identied search Engine. The result merging problem is to combine the documents returned from multiple search Engines. We will also point out some problems that need to be further researched

  • A Highly Scalable and Effective Method for
    2015
    Co-Authors: Weiyi Meng
    Abstract:

    A Metasearch Engine is a system that supports unified access to multiple local search Engines. Database selection is one of the main challenges in building a large-scale Metasearch Engine. The problem is to efficiently and accurately determine a small number of potentially useful local search Engines to invoke for each user query. In order to enable accurate selection, metadata that reflect the contents of each search Engine need to be collected and used. This article proposes a highly scalable and accurate database selection method. This method has several novel features. First, the metadata for representing the contents of all search Engines are organized into a single integrated representative. Such a representative yields both computational efficiency and storage efficiency. Second, the new selection method is based on a theory for ranking search Engines optimally. Exper-imental results indicate that this new method is very effective. An operational prototype system has been built based on the proposed approach

  • design and implementation of metta a Metasearch Engine for biomedical literature retrieval intended for systematic reviewers
    Health Information Science, 2014
    Co-Authors: Neil R Smalheiser, Yu Jiang, Can Lin, Lifeng Jia, Aaron Cohen, John M Davis, Clive E Adams, Marian Mcdonagh, Weiyi Meng
    Abstract:

    Background Individuals and groups who write systematic reviews and meta-analyses in evidence-based medicine regularly carry out literature searches across multiple search Engines linked to different bibliographic databases, and thus have an urgent need for a suitable Metasearch Engine to save time spent on repeated searches and to remove duplicate publications from initial consideration. Unlike general users who generally carry out searches to find a few highly relevant (or highly recent) articles, systematic reviewers seek to obtain a comprehensive set of articles on a given topic, satisfying specific criteria. This creates special requirements and challenges for Metasearch Engine design and implementation.

Sherry Koshman - One of the best experts on this subject based on the ideXlab platform.

  • web searcher interaction with the dogpile com Metasearch Engine
    Journal of the Association for Information Science and Technology, 2007
    Co-Authors: Bernard J Jansen, Amanda Spink, Sherry Koshman
    Abstract:

    Metasearch Engines are an intuitive method for improving the performance of Web search by increasing coverage, returning large numbers of results with a focus on relevance, and presenting alternative views of information needs. However, the use of Metasearch Engines in an operational environment is not well understood. In this study, we investigate the usage of Dogpile.com, a major Web Metasearch Engine, with the aim of discovering how Web searchers interact with Metasearch Engines. We report results examining 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005 and compare these results with findings from other Web searching studies. We collect data on geographical location of searchers, use of system feedback, content selection, sessions, queries, and term usage. Findings show that Dogpile.com searchers are mainly from the USA (84p of searchers), use about 3 terms per query (mean 5 2.85), implement system feedback moderately (8.4p of users), and generally (56p of users) spend less than one minute interacting with the Web search Engine. Overall, Metasearchers seem to have higher degrees of interaction than searchers on non-Metasearch Engines, but their sessions are for a shorter period of time. These aspects of Metasearching may be what define the differences from other forms of Web searching. We discuss the implications of our findings in relation to Metasearch for Web searchers, search Engines, and content providers. © 2007 Wiley Periodicals, Inc.

  • web searcher interaction with the dogpile com Metasearch Engine
    Office of Education Research; Faculty of Education, 2007
    Co-Authors: Bernard J Jansen, Amanda Spink, Sherry Koshman
    Abstract:

    Metasearch Engines are an intuitive method for improving the performance of Web search by increasing coverage, returning large numbers of results with a focus on relevance, and presenting alternative views of information needs. However, the use of Metasearch Engines in an operational environment is not well understood. In this study, we investigate the usage of Dogpile.com, a major Web Metasearch Engine, with the aim of discovering how Web searchers interact with Metasearch Engines. We report results examining 2,465,145 interactions from 534,507 users of Dogpile.com on May 6, 2005 and compare these results with findings from other Web searching studies. We collect data on geographical location of searchers, use of system feedback, content selection, sessions, queries, and term usage. Findings show that Dogpile.com searchers are mainly from the USA (84% of searchers), use about 3 terms per query (mean = 2.85), implement system feedback moderately (8.4% of users), and generally (56% of users) spend less than one minute interacting with the Web search Engine. Overall, Metasearchers seem to have higher degrees of interaction than searchers on non-Metasearch Engines, but their sessions are for a shorter period of time. These aspects of Metasearching may be what define the differences from other forms of Web searching. We discuss the implications of our findings in relation to Metasearch for Web searchers, search Engines, and content providers.

Neil R Smalheiser - One of the best experts on this subject based on the ideXlab platform.

  • Original article Rule-based deduplication of article records from bibliographic databases
    2016
    Co-Authors: Yu Jiang, Can Lin, Weiyi Meng, Aaron M. Cohen, Neil R Smalheiser
    Abstract:

    Vol. 2014: article ID bat086; doi:10.1093/database/bat086. We recently designed and deployed a Metasearch Engine, Metta, that sends queries and retrieves search results from fiv

  • design and implementation of metta a Metasearch Engine for biomedical literature retrieval intended for systematic reviewers
    Health Information Science, 2014
    Co-Authors: Neil R Smalheiser, Yu Jiang, Can Lin, Lifeng Jia, Aaron Cohen, John M Davis, Clive E Adams, Marian Mcdonagh, Weiyi Meng
    Abstract:

    Background Individuals and groups who write systematic reviews and meta-analyses in evidence-based medicine regularly carry out literature searches across multiple search Engines linked to different bibliographic databases, and thus have an urgent need for a suitable Metasearch Engine to save time spent on repeated searches and to remove duplicate publications from initial consideration. Unlike general users who generally carry out searches to find a few highly relevant (or highly recent) articles, systematic reviewers seek to obtain a comprehensive set of articles on a given topic, satisfying specific criteria. This creates special requirements and challenges for Metasearch Engine design and implementation.

  • rule based deduplication of article records from bibliographic databases
    Database, 2014
    Co-Authors: Yu Jiang, Can Lin, Weiyi Meng, A Arjeh M Cohen, Neil R Smalheiser
    Abstract:

    We recently designed and deployed a Metasearch Engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments.

  • Evidence-Based Medicine, the Essential Role of Systematic Reviews, and the Need for Automated Text Mining Tools
    2013
    Co-Authors: Aaron M. Cohen, Weiyi Meng, John M Davis, Clive E Adams, Marian Mcdonagh, Lorna Duggan, Neil R Smalheiser
    Abstract:

    High quality, cost-effective medical care requires consideration of the best available, most appropriate evidence in the care of each patient, a practice known as Evidence-based Medicine (EBM). EBM is dependent upon the wide availability and coverage of accurate, objective syntheses called evidence reports (also called systematic reviews). These are compiled by a time and resourceintensive process that is largely manual, and that has not taken advantage of many of the advances in information processing technologies that have assisted other textual domains. We propose a specific text-mining based pipeline to support the creation and updating of evidence reports that provides support for the literature collection, collation, and triage steps of the systematic review process. The pipeline includes a Metasearch Engine that covers both bibliographic databases and selected “grey ” literature

  • Health Information Science and Systems
    2013
    Co-Authors: Neil R Smalheiser, Yu Jiang, Weiyi Meng, Lifeng Jia, John M Davis, Clive E Adams, Marian S Mcdonagh, Aaron M. Cohen
    Abstract:

    This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. Design and implementation of metta, a Metasearch Engine for biomedical literature retrieval intended for systematic reviewer

Mireia Leg - One of the best experts on this subject based on the ideXlab platform.

Zhou Lihua - One of the best experts on this subject based on the ideXlab platform.

  • research on intelligent Metasearch Engine based on agent
    Computer Science, 2008
    Co-Authors: Zhou Lihua
    Abstract:

    A intelligent Metasearch Engine system based on multi-agent is proposed.The agent technique and information filtering technique based on personalized models are utilized,which makes the system more inteligent.The retrieval methods combining customized search with classified browse help users find relevant results more quickly.The scheduling of search sources is optimized by integrating the database categorization with virtual language model approach.A result merging method based on text /rank analysis and group decision making activity is presented.By utilizing text-based information such as title and snippets obtained from search results,the method to analyze the relevancy of title and snippets is described.Then,the relevant scores of the relevant documents are normalized by incorporating text analysis together with rank.Finally,a merging method based on group decision making activity is adopted to sort the search results.The experimental results show that this system has a better performance.