We present a framework for constructing a specific type of knowledge graph, a concept map<\/i> from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A traditional approach for concept map extraction consists of two sub-problems: key concept extraction and concept relationship identification. Previous work for the most part had considered these two sub-problems independently. We propose a framework that jointly optimizes these sub-problems and investigates methods that identify concept relationships. Experiments on concept maps that are manually extracted in six educational areas (computer networks, macroeconomics, precalculus, databases, physics, and geometry) show that our model outperforms supervised learning baselines that solve the two sub-problems separately. Moreover, we observe that incorporating textbook information helps with concept map extraction.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a framework for constructing a specific type of knowledge graph, a concept map from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A traditional approach for concept map extraction consists of two sub-problems: key concept extraction and concept relationship identification. Previous work for the most part had considered these two sub-problems […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Shuting Wang","user_id":0},{"type":"text","value":"Alexander G. Ororbia","user_id":0},{"type":"text","value":"Zhaohui Wu","user_id":0},{"type":"user_nicename","value":"kywillia","user_id":36404},{"type":"text","value":"Chen Liang","user_id":0},{"type":"text","value":"Bart Pursel","user_id":0},{"type":"text","value":"C. 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