02663nas a2200361   4500000000100000000000100001000000100002008004100003260000800044653002400052653002200076653002400098653002100122653002300143653003300166653003400199653002900233653003500262653003300297653002700330653001300357653002200370653001400392100002000406700002500426700002400451700002500475245009300500856014000593490000700733520154100740022002002281       2018                            d  csep10aAssessment accuracy10aBayesian networks10aConventional models10aExpert knowledge10aExplicit knowledge10aIntangible cultural heritage10aIntangible cultural heritages10aKnowledge representation10aMulti-Entity Bayesian Networks10aMultimodal Semantic Analysis10aOntological frameworks10aOntology10aSemantic analysis10aSemantics1 aGiannis Chantas1 aSotiris Karavarsamis1 aSpiros Nikolopoulos1 aIoannis Kompatsiaris00aA Probabilistic, Ontological Framework for Safeguarding the Intangible Cultural Heritage  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85051823861&doi=10.1145%2f3131610&partnerID=40&md5=62211d3ac16aa6c3bea8708652d2c7060 v113 aIn this article, we propose Multi-Entity Bayesian Networks (MEBNs) as the probabilistic ontological framework for the analysis of the Tsamiko and Salsa dances. More specifically, our analysis has the objective of the dancer assessment with respect to both choreography execution accuracy and the synchronization of the dance movements with the musical rhythm. For this task, we make use of the explicit, expert-provided knowledge on dance movements and their relations to the musical beat. Due to the complexity of this knowledge, the MEBNs were used as the probabilistic ontological framework in which the knowledge is formalized. The reason we opt for MEBNs for this task is that they combine Bayesian and formal (first-order) logic into a single model. In this way, the Bayesian probabilistic part of MEBNs was used to capture, using example data and training, the implicit part of the expert knowledge about dances, i.e., this part of the knowledge that cannot be formalized and explicitly defined accurately enough, while the logical maintains the explicit knowledge representation in the same way ontologies do. Moreover, we present in detail the MEBN models we built for Tsamiko and Salsa, using expert-provided explicit knowledge. Last, we conduct experiments that demonstrate the effectiveness of the proposed MEBN-based methodology we employ to achieve our analysis objectives. The results of the experiments demonstrate the superiority of MEBNs to conventional models, such as BNs, in terms of the dancer assessment accuracy.  a15564673 (ISSN)