A hybrid intelligent approach for the support of higher education students in literature discovery
dc.contributor.author | Prater, Ryan | |
dc.contributor.author | Laurenzi, Emanuele | |
dc.contributor.editor | Martin, Andreas | |
dc.contributor.editor | Hinkelmann, Knut | |
dc.contributor.editor | Fill, Hans-Georg | |
dc.contributor.editor | Gerber, Aurona | |
dc.contributor.editor | Lenat, Doug | |
dc.contributor.editor | Stolle, Reinhard | |
dc.contributor.editor | van Harmelen, Frank | |
dc.date.accessioned | 2024-04-19T05:50:55Z | |
dc.date.available | 2024-04-19T05:50:55Z | |
dc.date.issued | 2022 | |
dc.description.abstract | In this paper, we present a hybrid intelligent approach that combines knowledge engineering, machine learning, and human intervention to automatically recommend literature resources relevant for a high quality of literature discovery. The primary target group that we aim to support is higher education students in their first experiences with research works. The approach builds a knowledge graph by leveraging a logistic regression algorithm which is first parameterized and then influenced by the interventions of a supervisor and a student, respectively. Both interventions allow continuous learning based on both the supervisor’s preferences (e.g. high score of H-index) and the student’s feedback to the resulting literature resources. The creation of the hybrid intelligent approach followed the Design-Science Research methodology and is instantiated in a working prototype named PaperZen. The evaluation was conducted in two complementary ways: (1) by showing how the design requirements manifest in the prototype, and (2) with an illustrative scenario in which a corpus of a research project was taken as a source of truth. A small subset from the corpus was entered into the PaperZen and Google Scholar, independently. The resulting literature resources were compared with the corpus of a research project and show that PaperZen outperforms Google Scholar | |
dc.description.uri | https://ceur-ws.org/Vol-3121/ | |
dc.event | AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022) | |
dc.event.end | 2022-03-23 | |
dc.event.start | 2022-03-21 | |
dc.identifier.uri | https://irf.fhnw.ch/handle/11654/43342 | |
dc.identifier.uri | https://doi.org/10.26041/fhnw-7307 | |
dc.language.iso | en | |
dc.relation.ispartof | Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.spatial | Palo Alto | |
dc.subject.ddc | 330 - Wirtschaft | |
dc.title | A hybrid intelligent approach for the support of higher education students in literature discovery | |
dc.type | 04B - Beitrag Konferenzschrift | |
dspace.entity.type | Publication | |
fhnw.InventedHere | Yes | |
fhnw.ReviewType | Anonymous ex ante peer review of a complete publication | |
fhnw.affiliation.hochschule | Hochschule für Wirtschaft | de_CH |
fhnw.affiliation.institut | Institut für Wirtschaftsinformatik | de_CH |
fhnw.openAccessCategory | Diamond | |
fhnw.publicationState | Published | |
relation.isAuthorOfPublication | 1629b3c4-7073-4be7-aff7-485e4b94fb42 | |
relation.isAuthorOfPublication | 4a2b6cad-6ed6-4355-a377-e408a177b079 | |
relation.isAuthorOfPublication.latestForDiscovery | 4a2b6cad-6ed6-4355-a377-e408a177b079 | |
relation.isEditorOfPublication | 6a3865e7-85dc-41b5-afe3-c834c56fab4e | |
relation.isEditorOfPublication | 6898bec4-c71c-491e-b5f8-2b1cba9cfa00 | |
relation.isEditorOfPublication.latestForDiscovery | 6a3865e7-85dc-41b5-afe3-c834c56fab4e |
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