@inproceedings{3943,
  keywords = {Artificial intelligence, Back-ground knowledge, Intangible cultural heritages, Malaysians, ontologies, Ontology, Query answering, Querying, Semantics, Video Annotation, Video annotations, Video datasets, Video training},
  author = {Sihem Belabbes and Chi Tan and Tri-Thuc Vo and Yacine Izza and Karim Tabia and Sylvain Lagrue and Salem Benferhat and IEEE},
  title = {Query Answering from Traditional Dance Videos: Case Study of Zapin Dances},
  abstract = {The aim of this paper is to highlight two important issues related to the annotation and querying of Intangible Cultural Heritage video datasets. First, we focus on ontology completion by annotating dance videos. In order to build video training sets and to enrich the proposed ontology, manual video annotation is performed based on background knowledge formalized in an ontology, representing a semantics of a traditional dance. The paper provides a case study on Malaysian Zapin dances. Second, we address the question of how can end-users efficiently query the datasets of annotated videos that are built.},
  year = {2019},
  booktitle = {Proc. Int. Conf. Tools Artif. Intell. ICTAI},
  volume = {2019-November},
  pages = {1638-1642},
  publisher = {IEEE Computer Society},
  school = {IEEE Computer Society},
  isbn = {10823409 (ISSN); 9781728137988 (ISBN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081078720&doi=10.1109%2fICTAI.2019.00239&partnerID=40&md5=b70bff9d438c3dae6cae56cd746fab0c},
  doi = {10.1109/ICTAI.2019.00239},
  note = {Journal Abbreviation: Proc. Int. Conf. Tools Artif. Intell. ICTAI},
  language = {English},
}