04080nas a2200469 a 4500 20260817163819.0 240229s2022 th u m tt 000 a eng d .b12421674 AIT Thesis no.DSAI-22-05 Natnaree Sornkongdang An attention and concept hierarchy-based approach to dataset category and tag recommendation Pathum Thani, Thailand : Asian Institute of Technology, 2022 109 leaves : ill. Thesis ; no. DSAI-22-05 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence, School of Engineering and Technology Thesis (M. Sc.) - Asian Institute of Technology, 2022 The improper tag organization has been derived by data providers who provide data categories and tags for a dataset to be published on the ThOGD portal. They have currently guided by the available autocomplete function in the portal. With this application, data categories and tags to be suggested to data providers are forecasted from the historical data that was provided by previous data providers. This results to a consequence of several datasets with similar contents but are labeled with different tags in similar meaning are found in the portal. Besides, data consumers cannot get the information being matched to their preference according to the filtering of data category and tag. In this study, our contributions for overcoming the above-mentioned challenges have two main sections, including the attention-based categorical identifier and the topic hierarchy-based categorical concept hierarchies. With the use of Attentive Deep Supervision, there is a weighted effect on loss optimization of the categorical identifier. With the use of Topic Hierarchy, Latent Dirichlet Allocation (LDA) topic modeling is utilized for potential tag term extraction, Heterogeneous Evidences are exploited for relation identification, and Anytree is employed for hierarchy construction. By applying these approaches, the macro average of precision and F1-score of the attention-based identifier improves by 0.6640 % and 0.5570 %, respectively. The micro average improves by 0.8060 %, and 0.6980 %, successively. Meanwhile, the concept hierarchy based categorical concept hierarchies can provide comprehensive tags related to a dataset to be published because of the recommendation strategy that assigning tags with the same highest important weight to the same rank. Machine learning Information retrieval Recommender systems (Information filtering) Chutiporn Anutariya, Chairperson Dailey, Matthew N., Examination Committee Nuttapong Sanglerdsinlapachai, Examination Committee AIT Scholarships, Scholarship Donor Asian Institute of Technology. Thesis ; no. DSAI-22-05 Full-Text http://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B20418 .b12421674 mnait a 250428 0 240313 m a a 3 mnarc mnarc mnait mnait 67 40 20 Barcode : - CREATED : 2024-02-29 RECORD # : i13487887 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120899058 CREATED : 2025-07-03 RECORD # : i13537143 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120425110 CREATED : 2025-08-04 RECORD # : i13542989 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 Barcode : 30050120425102 CREATED : 2025-08-04 RECORD # : i13542990 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 9856 9856 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Thesis no.DSAI-22-05 2026-08-17 16:38:19 2026-08-17 67 0 0 0 0 MAIN MAIN mnarc 2026-08-17 0 AIT Thesis no.DSAI-22-05 30050120899058 2026-08-17 16:38:19 1 2026-08-17 40 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Thesis no.DSAI-22-05 30050120425110 2026-08-17 16:38:19 1 2026-08-17 20 0 0 0 0 MAIN MAIN mnait 2026-08-17 50.00 0 AIT Thesis no.DSAI-22-05 30050120425102 2026-08-17 16:38:19 2 2026-08-17 20