01768nas a2200397   4500000000100000000000100001008004100002260002600043653002800069653002600097653003400123653001500157653001500172653001300187653002000200653001300220653001400233653002100247653002200268653001900290653001900309100001900328700001200347700001600359700001600375700001600391700001900407700002000426700000900446245007800455856014900533300001400682490001800696520061400714020004201328       2019                            d  bIEEE Computer Society10aArtificial intelligence10aBack-ground knowledge10aIntangible cultural heritages10aMalaysians10aontologies10aOntology10aQuery answering10aQuerying10aSemantics10aVideo Annotation10aVideo annotations10aVideo datasets10aVideo training1 aSihem Belabbes1 aChi Tan1 aTri-Thuc Vo1 aYacine Izza1 aKarim Tabia1 aSylvain Lagrue1 aSalem Benferhat1 aIEEE00aQuery Answering from Traditional Dance Videos: Case Study of Zapin Dances  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081078720&doi=10.1109%2fICTAI.2019.00239&partnerID=40&md5=b70bff9d438c3dae6cae56cd746fab0c  a1638-16420 v2019-November3 aThe 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.  a10823409 (ISSN); 9781728137988 (ISBN)