%0 Conference Paper %1 citeulike:14220348 %A Wang, Shuting %A Ororbia, Alexander %A Wu, Zhaohui %A Williams, Kyle %A Liang, Chen %A Pursel, Bart %A Giles, C. Lee %B Proceedings of the 25th ACM International on Conference on Information and Knowledge Management %C New York, NY, USA %D 2016 %I ACM %K concept-extraction %P 317--326 %R 10.1145/2983323.2983725 %T Using Prerequisites to Extract Concept Maps fromTextbooks %U http://dx.doi.org/10.1145/2983323.2983725 %X 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 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. %@ 978-1-4503-4073-1

@inproceedings{citeulike:14220348, abstract = {{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 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.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {New York, NY, USA}, author = {Wang, Shuting and Ororbia, Alexander and Wu, Zhaohui and Williams, Kyle and Liang, Chen and Pursel, Bart and Giles, C. Lee}, biburl = {https://www.bibsonomy.org/bibtex/2cd10bdae945d17d555dacdef0f9c7426/aho}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, citeulike-article-id = {14220348}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2983725}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/2983323.2983725}, doi = {10.1145/2983323.2983725}, interhash = {7390143e542b3c7085c7d4860044a71a}, intrahash = {cd10bdae945d17d555dacdef0f9c7426}, isbn = {978-1-4503-4073-1}, keywords = {concept-extraction}, location = {Indianapolis, Indiana, USA}, pages = {317--326}, posted-at = {2016-12-05 16:11:25}, priority = {3}, publisher = {ACM}, series = {CIKM '16}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{Using Prerequisites to Extract Concept Maps fromTextbooks}}, url = {http://dx.doi.org/10.1145/2983323.2983725}, year = 2016 }