As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge.
Towards the identification of propaedeutic relations in textbooks
Alzetta C.;
2019
Abstract
As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.anceserie | LECTURE NOTES IN COMPUTER SCIENCE | en |
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | en |
| dc.authority.people | Adorni G. | en |
| dc.authority.people | Alzetta C. | en |
| dc.authority.people | Koceva F. | en |
| dc.authority.people | Passalacqua S. | en |
| dc.authority.people | Torre I. | en |
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| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
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| dc.date.accessioned | 2024/12/03 16:15:42 | - |
| dc.date.available | 2024/12/03 16:15:42 | - |
| dc.date.firstsubmission | 2024/08/27 15:02:05 | * |
| dc.date.issued | 2019 | - |
| dc.date.submission | 2024/08/27 15:02:05 | * |
| dc.description.abstracteng | As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge. | - |
| dc.description.allpeople | Adorni, G.; Alzetta, C.; Koceva, F.; Passalacqua, S.; Torre, I. | - |
| dc.description.allpeopleoriginal | Adorni G.; Alzetta C.; Koceva F.; Passalacqua S.; Torre I. | en |
| dc.description.fulltext | none | en |
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| dc.description.numberofauthors | 5 | - |
| dc.identifier.doi | 10.1007/978-3-030-23204-7_1 | en |
| dc.identifier.isbn | 9783030232030 | en |
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| dc.language.iso | eng | en |
| dc.publisher.name | Springer Verlag | en |
| dc.relation.allauthors | Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. | en |
| dc.relation.firstpage | 1 | en |
| dc.relation.ispartofbook | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
| dc.relation.lastpage | 13 | en |
| dc.relation.numberofpages | 13 | en |
| dc.relation.volume | 11625 | en |
| dc.subject.keywords | Knowledge structure | - |
| dc.subject.keywords | Relation extraction | - |
| dc.subject.keywords | Temporal reasoning | - |
| dc.subject.singlekeyword | Knowledge structure | * |
| dc.subject.singlekeyword | Relation extraction | * |
| dc.subject.singlekeyword | Temporal reasoning | * |
| dc.title | Towards the identification of propaedeutic relations in textbooks | en |
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| scopus.contributor.subaffiliation | Department of Informatics;Bioengineering;Robotics and Systems Engineering; | - |
| scopus.contributor.subaffiliation | Department of Informatics;Bioengineering;Robotics and Systems Engineering; | - |
| scopus.contributor.subaffiliation | Department of Informatics;Bioengineering;Robotics and Systems Engineering; | - |
| scopus.contributor.subaffiliation | Department of Informatics;Bioengineering;Robotics and Systems Engineering; | - |
| scopus.contributor.subaffiliation | Department of Informatics;Bioengineering;Robotics and Systems Engineering; | - |
| scopus.contributor.surname | Adorni | - |
| scopus.contributor.surname | Alzetta | - |
| scopus.contributor.surname | Koceva | - |
| scopus.contributor.surname | Passalacqua | - |
| scopus.contributor.surname | Torre | - |
| scopus.date.issued | 2019 | * |
| scopus.description.abstracteng | As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge. | * |
| scopus.description.allpeopleoriginal | Adorni G.; Alzetta C.; Koceva F.; Passalacqua S.; Torre I. | * |
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| scopus.relation.conferencedate | 2019 | * |
| scopus.relation.conferencename | 20th International Conference on Artificial Intelligence in Education, AIED 2019 | * |
| scopus.relation.conferenceplace | usa | * |
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| scopus.relation.lastpage | 13 | * |
| scopus.relation.volume | 11625 | * |
| scopus.subject.keywords | Knowledge structure; Relation extraction; Temporal reasoning; | * |
| scopus.title | Towards the identification of propaedeutic relations in textbooks | * |
| scopus.titleeng | Towards the identification of propaedeutic relations in textbooks | * |
| Appare nelle tipologie: | 02.01 Contributo in volume (Capitolo o Saggio) | |
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