Automatic Question Generation Based on Historical Panoramic Knowledge Graphs and Inference Rules
There has been a lot of research on using data from Wikipedia and other sources as a knowledge graph to generate questions for learning history and other subjects. These knowledge graphs consist of entities (words) and relations (links) between the entities, and the existing methods generated questi...
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| Published in | Transactions of the Japanese Society for Artificial Intelligence Vol. 40; no. 1; pp. B-O71_1 - 16 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
Tokyo
The Japanese Society for Artificial Intelligence
01.01.2025
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1346-0714 1346-8030 |
| DOI | 10.1527/tjsai.40-1_B-O71 |
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| Abstract | There has been a lot of research on using data from Wikipedia and other sources as a knowledge graph to generate questions for learning history and other subjects. These knowledge graphs consist of entities (words) and relations (links) between the entities, and the existing methods generated questions by extracting small subgraphs from the knowledge graphs and hiding target words (correct answer words). However, questions generated by existing methods can be solved with narrow knowledge, so they do not contribute to the development of panoramic ability that has been increasingly demanded in recent years. While increasing the size of the extracted subgraph enhances the panoramic of the question, if the subgraph is too large, it becomes difficult to understand and time-consuming to learn. Therefore, in this paper, our goal is to enhance the panoramic while keeping the subgraph small. Specifically, we prioritize extracting entities within the subgraph that are semantically distant from the correct answer word. Furthermore, we propose a method to add bypass links based on the inference rules to ensure that the extracted entities are connected to the correct answer word with minimal hops from the perspective of temporal and spatial panoramic knowledge. Since KGs based on Wikipedia do not represent all common knowledge, we utilize inference rules to complement the correct relations without contradictions. As a result of conducting subjective evaluation experiments with participants and objective evaluation experiments about the traversal of temporal and spatial knowledge from history subjects, it was confirmed that the proposed method can generate more panoramic and comprehensive questions in both temporal and spatial dimensions, at a similar scale to existing methods. |
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| AbstractList | There has been a lot of research on using data from Wikipedia and other sources as a knowledge graph to generate questions for learning history and other subjects. These knowledge graphs consist of entities (words) and relations (links) between the entities, and the existing methods generated questions by extracting small subgraphs from the knowledge graphs and hiding target words (correct answer words). However, questions generated by existing methods can be solved with narrow knowledge, so they do not contribute to the development of panoramic ability that has been increasingly demanded in recent years. While increasing the size of the extracted subgraph enhances the panoramic of the question, if the subgraph is too large, it becomes difficult to understand and time-consuming to learn. Therefore, in this paper, our goal is to enhance the panoramic while keeping the subgraph small. Specifically, we prioritize extracting entities within the subgraph that are semantically distant from the correct answer word. Furthermore, we propose a method to add bypass links based on the inference rules to ensure that the extracted entities are connected to the correct answer word with minimal hops from the perspective of temporal and spatial panoramic knowledge. Since KGs based on Wikipedia do not represent all common knowledge, we utilize inference rules to complement the correct relations without contradictions. As a result of conducting subjective evaluation experiments with participants and objective evaluation experiments about the traversal of temporal and spatial knowledge from history subjects, it was confirmed that the proposed method can generate more panoramic and comprehensive questions in both temporal and spatial dimensions, at a similar scale to existing methods. |
| ArticleNumber | 40-1-B-O71 |
| Author | Tahara, Yasuyuki Egami, Shusaku Ohsuga, Akihiko Okuhara, Fumika Sei, Yuichi |
| Author_xml | – sequence: 1 fullname: Okuhara, Fumika organization: Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications – sequence: 1 fullname: Sei, Yuichi organization: Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications – sequence: 1 fullname: Tahara, Yasuyuki organization: Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications – sequence: 1 fullname: Ohsuga, Akihiko organization: Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications – sequence: 1 fullname: Egami, Shusaku organization: Artificial Intelligence Research Center，National Institute of Advanced Industrial Science and Technology (AIST) |
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| Cites_doi | 10.18653/v1/K16-1025 10.1145/1376616.1376746 10.1007/s40593-019-00186-y 10.1007/s00354-016-0404-x 10.1007/s40593-018-00172-w 10.5220/0007259301100120 10.1007/s13218-015-0405-9 10.1145/182.358434 10.1007/978-3-540-76298-0_52 10.1145/2629489 10.1007/s11036-020-01726-w |
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R., Editors: OWLWeb Ontology Language Overview, http://www.w3.org/TR/2004/REC-owl-features-20040210/(2004) [Jouault 16] Jouault, C., Seta, K., and Hayashi, Y.: Contentdependentquestion generation using LOD for history learning inopen learning space, New Generation Computing, Vol. 34, No. 4,pp. 367–394 (2016) [Ministry of Education, Culture, Sports, Science and Technology 18]Ministry of Education, Culture, Sports, Science and Technology 文部科学省：【地理歴史編】高等学校学習指導要領（平成30 年告示）解説, https://www.mext.go.jp/a_menu/shotou/new-cs/1407074.htm (2018) [Kato 14] Kato, F., Takeda, H., Koide, S., and Ohmukai, I.: Buildingdbpedia japanese and linked data cloud in Japanese, in 2013 LinkedData in Practice Workshop (LDPW2013), pp. 1–11 (2014) [Mikolov 13] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. 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ACM, Vol. 57, No. 10, p. 78–85(2014) [Fukusaka 18] Fukusaka, S., Takagi, M., Yamada, K., and Sasaki, J.:問題自動生成システムを利用した作問演習の実践と評価, JSiSE研究会研究報告, Vol. 32, No. 5, pp. 107–114 (2018) [Central Council for Education 18] Central Council for Education 中央教育審議会：2040 年に向けた高等教育のグランドデザイン（答申）（中教審第211 号）(平成30 年11 月26 日),https://www.mext.go.jp/b_menu/shingi/chukyo/chukyo0/toushin/1411360.htm (2018) [Yang 21] Yang, Z., Wang, Y., Gan, J., Li, H., and Lei, N.: Designand research of intelligent question-answering (Q&A) system basedon high school course knowledge graph, Mobile Networks and Applications,Vol. 26, No. 5, pp. 1884–1890 (2021) [Intergovernmental Oceanographic Commission 22]Intergovernmental Oceanographic Commission, and UNESCOOffice Venice and Regional Bureau for Science and Culturein Europe (Italy), : A new blue curriculum: a toolkit forpolicy-makers, https://unesdoc.unesco.org/ark:/48223/pf0000380544 (2022) [Auer 07] Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak,R., and Ives, Z.: DBpedia: A nucleus for a web of open data, inAberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.,Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., andCudr´e-Mauroux, P. eds., The Semantic Web, pp. 722–735, SpringerBerlin Heidelberg (2007) [UNESCO Institute for Lifelong Learning 19] UNESCO Institute forLifelong Learning, : Addressing global citizenship education in adultlearning and education: summary report, https://unesdoc.unesco.org/ark:/48223/pf0000372425 (2019) [Bollacker 08] Bollacker, K., Evans, C., Paritosh, P., Sturge, T., andTaylor, J.: Freebase: a collaboratively created graph database forstructuring human knowledge, in Proceedings of the 2008 ACM SIGMODInternational Conference on Management of Data, pp. 1247–1250 (2008) [UNESCO International Bureau of Education 21] UNESCO InternationalBureau of Education, : Ten clues for rethinkingcurriculum, https://unesdoc.unesco.org/ark:/48223/pf0000387188 (2021) [The Association for High School-University Collaboration 17]The Association for High School-University Collaboration inHistory Education 高大連携歴史学習研究会：高等学校教科書および大学入試における歴史系用語精選の提案（第一次）,https://kodairekikyo.org/wp-content/uploads/2020/08/selection_plan_2017.pdf (2017) 22 23 24 25 26 27 28 10 11 12 13 14 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 21 |
| References_xml | – reference: [Mikolov 13] Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., andDean, J.: Distributed representations of words and phrases and theircompositionality, Advances in Neural Information Processing Systems,Vol. 26, (2013) – reference: [Michael K. Smith 04] Michael K. Smith, C. W. and DeborahL. McGuinness, W. R., Editors: OWL Web OntologyLanguage Guide, http://www.w3.org/TR/2004/REC-owl-guide-20040210/ (2004) – reference: [Zhao 22] Zhao, B., Sun, J., Xu, B., Lu, X., Li, Y., Yu, J., Liu, M.,Zhang, T., Chen, Q., Li, H., Hou, L., and Li, J.: EDUKG: a heterogeneoussustainable K-12 educational knowledge graph, https://arxiv.org/abs/2210.12228 (2022) – reference: [Graesser 09] Graesser, A., Ozuru, Y., and Sullins, J.: What is a goodquestion? In M. McKeown & G. Kucan (Eds.), Bringing readingresearch to life, Guilford Press, pp. 112–141 (2009) – reference: [Okuhara 19a] Okuhara, F., Sei, Y., Tahara, Y., and Ohsuga, A.: Generationof multiple choice questions including panoramic informationusing linked data, in Proceedings of the 11th International Conferenceon Agents and Artificial Intelligence (ICAART 2019),INSTICC,SciTePress (2019) – reference: [Jouault 16] Jouault, C., Seta, K., and Hayashi, Y.: Contentdependentquestion generation using LOD for history learning inopen learning space, New Generation Computing, Vol. 34, No. 4,pp. 367–394 (2016) – reference: [Kurdi 20] Kurdi, G., Leo, J., Parsia, B., Sattler, U., and Al-Emari, S.:A systematic review of automatic question generation for educationalpurposes, International Journal of Artificial Intelligence in Education,Vol. 30, No. 1, pp. 121–204 (2020) – reference: [UNESCO Institute for Lifelong Learning 19] UNESCO Institute forLifelong Learning, : Addressing global citizenship education in adultlearning and education: summary report, https://unesdoc.unesco.org/ark:/48223/pf0000372425 (2019) – reference: [UNESCO International Bureau of Education 21] UNESCO InternationalBureau of Education, : Ten clues for rethinkingcurriculum, https://unesdoc.unesco.org/ark:/48223/pf0000387188 (2021) – reference: [Allen 83] Allen, J.: Maintaining knowledge about temporal intervals,Communications of ACM, Vol. 26, No. 11, pp. 832–843 (1983) – reference: [Leo 19] Leo, J., Kurdi, G., Matentzoglu, N., Parsia, B., Sattler, U.,Forge, S., Donato, G., and Dowling, W.: Ontology-based generationof medical, multi-term MCQs, International Journal of Artificial Intelligencein Education, Vol. 29, No. 2, pp. 145–188 (2019) – reference: [Auer 07] Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak,R., and Ives, Z.: DBpedia: A nucleus for a web of open data, inAberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.,Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., andCudr´e-Mauroux, P. eds., The Semantic Web, pp. 722–735, SpringerBerlin Heidelberg (2007) – reference: [Fukusaka 18] Fukusaka, S., Takagi, M., Yamada, K., and Sasaki, J.:問題自動生成システムを利用した作問演習の実践と評価, JSiSE研究会研究報告, Vol. 32, No. 5, pp. 107–114 (2018) – reference: [Worldwide Learning Consortium 23] Worldwide Learning Consortium,: Support Project for Building the WWL (Worldwide Learning)Consortium, https://b-wwl.jp/wp/wp-content/uploads/2023/04/R4WWL_en.pdf (2023) – reference: [Kato 14] Kato, F., Takeda, H., Koide, S., and Ohmukai, I.: Buildingdbpedia japanese and linked data cloud in Japanese, in 2013 LinkedData in Practice Workshop (LDPW2013), pp. 1–11 (2014) – reference: [Zablith 22] Zablith, F.: Constructing social media links to formallearning: A knowledge Graph Approach, Educational TechnologyResearch and Development, pp. 1–26 (2022) – reference: [Bollacker 08] Bollacker, K., Evans, C., Paritosh, P., Sturge, T., andTaylor, J.: Freebase: a collaboratively created graph database forstructuring human knowledge, in Proceedings of the 2008 ACM SIGMODInternational Conference on Management of Data, pp. 1247–1250 (2008) – reference: [Alsubait 16] Alsubait, T., Parsia, B., and Sattler, U.: Ontologybasedmultiple choice question generation, KI-K¨unstliche Intelligenz,Vol. 30, No. 2, pp. 183–188 (2016) – reference: [Okuhara 19b] Okuhara, F., Sei, Y., Tahara, Y., and Ohsuga, A.:Linked Data を用いた俯瞰的な多肢選択式問題自動生成手法の提案, 情報処理学会論文誌, Vol. 60, No. 10, pp. 1738–1756 (2019) – reference: [Intergovernmental Oceanographic Commission 22]Intergovernmental Oceanographic Commission, and UNESCOOffice Venice and Regional Bureau for Science and Culturein Europe (Italy), : A new blue curriculum: a toolkit forpolicy-makers, https://unesdoc.unesco.org/ark:/48223/pf0000380544 (2022) – reference: [McGuinness 04] McGuinness, D. L. and Harmelen, Frank vanW. R., Editors: OWLWeb Ontology Language Overview, http://www.w3.org/TR/2004/REC-owl-features-20040210/(2004) – reference: [Yang 21] Yang, Z., Wang, Y., Gan, J., Li, H., and Lei, N.: Designand research of intelligent question-answering (Q&A) system basedon high school course knowledge graph, Mobile Networks and Applications,Vol. 26, No. 5, pp. 1884–1890 (2021) – reference: [The Association for High School-University Collaboration 17]The Association for High School-University Collaboration inHistory Education 高大連携歴史学習研究会：高等学校教科書および大学入試における歴史系用語精選の提案（第一次）,https://kodairekikyo.org/wp-content/uploads/2020/08/selection_plan_2017.pdf (2017) – reference: [Vrandeˇci´c 14] Vrandeˇci´c, D. and Kr¨otzsch, M.: Wikidata: a free collaborativeknowledgebase, Commun. ACM, Vol. 57, No. 10, p. 78–85(2014) – reference: [Yamada 16] Yamada, I., Shindo, H., Takeda, H., and Takefuji, Y.:Joint learning of the embedding of words and entities for named entitydisambiguation, https://arxiv.org/abs/1601.01343(2016) – reference: [Matsumoto 18] Matsumoto, Y. and Murakawa, T.: 情報リテラシーを対象とした理解度テストの生成と分析, 2018 年度情報処理学会関西支部支部大会講演論文集, Vol. 2018, (2018) – reference: [Central Council for Education 18] Central Council for Education 中央教育審議会：2040 年に向けた高等教育のグランドデザイン（答申）（中教審第211 号）(平成30 年11 月26 日),https://www.mext.go.jp/b_menu/shingi/chukyo/chukyo0/toushin/1411360.htm (2018) – reference: [Ministry of Education, Culture, Sports, Science and Technology 18]Ministry of Education, Culture, Sports, Science and Technology 文部科学省：【地理歴史編】高等学校学習指導要領（平成30 年告示）解説, https://www.mext.go.jp/a_menu/shotou/new-cs/1407074.htm (2018) – ident: 25 doi: 10.18653/v1/K16-1025 – ident: 4 doi: 10.1145/1376616.1376746 – ident: 10 – ident: 11 doi: 10.1007/s40593-019-00186-y – ident: 16 – ident: 9 doi: 10.1007/s00354-016-0404-x – ident: 12 doi: 10.1007/s40593-018-00172-w – ident: 14 – ident: 28 – ident: 24 – ident: 7 – ident: 20 – ident: 22 – ident: 17 – ident: 18 doi: 10.5220/0007259301100120 – ident: 5 – ident: 2 doi: 10.1007/s13218-015-0405-9 – ident: 1 doi: 10.1145/182.358434 – ident: 19 – ident: 13 – ident: 15 – ident: 3 doi: 10.1007/978-3-540-76298-0_52 – ident: 6 – ident: 8 – ident: 23 doi: 10.1145/2629489 – ident: 26 doi: 10.1007/s11036-020-01726-w – ident: 21 – ident: 27 |
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| Title | Automatic Question Generation Based on Historical Panoramic Knowledge Graphs and Inference Rules |
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