کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946490 1439286 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning the heterogeneous bibliographic information network for literature-based discovery
ترجمه فارسی عنوان
یادگیری شبکه اطلاعات کتابشناختی ناهمگن برای کشف مبتنی بر ادبیات
کلمات کلیدی
کشف مبتنی بر ادبیات، شبکه اطلاعاتی چندگانه، پیش بینی پیوند،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

This paper presents HBIN-LBD, a novel literature-based discovery (LBD) method that exploits the lexico-citation structures within the heterogeneous bibliographic information network (HBIN) graphs. Unlike other existing LBD methods, HBIN-LBD harnesses the metapath features found in HBIN graphs for discovering the latent associations between scientific papers published in otherwise disconnected research areas. Further, this paper investigates the effects of incorporating semantic and topic modeling components into the proposed models. Using time-sliced historical bibliographic data, we demonstrate the performance of our method by reconstructing two LBD hypotheses: the Fish Oil and Raynaud's Syndrome hypothesis and the Migraine and Magnesium hypothesis. The proposed method is capable of predicting the future co-citation links between research papers of these previously disconnected research areas with up to 88.86% accuracy and 0.89 F-measure.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 115, 1 January 2017, Pages 66-79
نویسندگان
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