CS6604: Digital Libraries

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https://hdl.handle.net/10919/47780

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Browsing CS6604: Digital Libraries by Subject "deep learning"

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    ACM Venue Recommendation System
    Kumar, Harinni Kodur; Tyagi, Tanya (Virginia Tech, 2019-12-23)
    A frequent goal of a researcher is to publish his/her work in appropriate conferences and journals. With a large number of options for venues in the microdomains of every research discipline, the issue of selecting suitable locations for publishing cannot be underestimated. Further, the venues diversify themselves in the form of workshops, symposiums, and challenges. Several publishers such as IEEE and Springer have recognized the need to address this issue and have developed journal recommenders. In the proposed project, the goal is to design and develop similar a recommendation system for the ACM dataset. The conventional approach to building such a recommendation system is to utilize the content features in a dataset through content-based and collaborative approaches and proffer suggestions. An alternative is to view this recommendation problem from a classification perspective. With the success of deep learning classifiers in recent times and their pervasiveness in several domains, our goal is to solve the problem of recommending conference and journal venues by incorporating deep learning methodologies given some information about the submission like title, keywords, abstract, etc. The dataset used for the project is the ACM Digital Library metadata that includes metadata and textual information for research papers and journals submitted at various conferences and journals over the past 60 years. Our current system offers recommendations based on 80 binary classifiers. From our results, we could observe that for past submissions, our system recommends ground truth venues precisely. In the subsequent iterations of the project, we aim to improve the performance of individual classifiers and thereby offer better recommendations.
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    ETDseer Concept Paper
    Ma, Yufeng; Jiang, Tingting; Shrestha, Chandani (Virginia Tech, 2017-05-03)
    ETDSeer (electronic thesis and dissertation digital library connected with SeerSuite) will build on 15 years of collaboration between teams at Virginia Tech (VT) and Penn State University (PSU), since both have been leaders in the worldwide digital library (DL) community. VT helped launch the national and international efforts for ETDs more than 20 years ago, which have been led by the Networked Digital Library of Theses and Dissertations (NDLTD, directed by PI Fox); its Union Catalog has increased to 5 million records. PSU hosts CiteSeerX, which co-PI Giles launched almost 20 years ago, and which is connected with a wide variety of research results under the SeerSuite family. ETDs, typically in PDF, are a largely untapped international resource. Digital libraries with advanced services can effectively address the broad needs to discover and utilize ETDs of interest. Our research will leverage SeerSuite methods that have been applied mostly to short documents, plus a variety of exploratory studies at VT, and will yield a “web of graduate research”, rich knowledge bases, and a digital library with effective interfaces. References will be analyzed and converted to canonical forms, figures and tables will be recognized and re-represented for flexible searching, small sections (acknowledgments, biographical sketches) will be mined, and aids for researchers will be built especially from literature reviews and discussions of future work. Entity recognition and disambiguation will facilitate flexible use of a large graph of linked open data.