کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4944140 1363983 2018 16 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Network-based approach to detect novelty of scholarly literature
ترجمه فارسی عنوان
رویکرد مبتنی بر شبکه برای تشخیص نوآوری ادبیات علمی
کلمات کلیدی
تشخیص نوآوری؛ ادبیات علمی؛ شبکه عصبی خودرمزگذار ؛ استخراج ویژگی های مبتنی بر شبکه؛
Novelty detection; Scholarly literature; Autoencoder neural network; Network-based feature extraction;
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

We present a method to detect the novelty of a research paper. Because novelty in scholarly literature also examines the larger research community, a network-based approach for extracting features is proposed. Two graphs are introduced, a macro-level graph, where authors and documents are used as nodes, and a micro-level graph, where keywords, topics, and words are used as nodes. After constructing the seed graph, papers are incrementally added while changes in the graph are recorded as the feature set of a paper. An autoencoder neural network is then used as the novelty detection model. The experimental results show that the commonly used text feature representations, TF-IDF and one-class SVM, are not suitable for detecting the novelty of a research paper. Among the constructed graphs, keyword-level graph features exhibit the best performance using regression analysis as the metric. We also combine the macro-level graph, micro-level graph, and all features and find that the combination of keywords, topics, and word features perform the best using regression and citation count analysis. Other factors that could affect the citation counts, impact, and audience, are also discussed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 422, January 2018, Pages 542-557
نویسندگان
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