01265nas a2200193   4500000000100000008004100001100002000042700002600062700002400088700002500112700001700137700002500154700002200179245008000201300001200281490000900293520075500302020001401057                                       d1 aGiannis Chantas1 aAlexandros Kitsikidis1 aSpiros Nikolopoulos1 aKosmas Dimitropoulos1 aStella Douka1 aIoannis Kompatsiaris1 aNikos Grammalidis00aMulti-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content  a355-3690 v89263 aIn this paper we introduce Multi-Entity Bayesian Networks (MEBNs) as the means to combine first-order logic with probabilistic inference and facilitate the semantic analysis of Intangible Cultural Heritage (ICH) content. First, we mention the need to capture and maintain ICH manifestations for the safeguarding of cultural treasures. Second, we present the MEBN models and stress their key features that can be used as a powerful tool for the aforementioned cause. Third, we present the methodology followed to build a MEBN model for the analysis of a traditional dance. Finally, we compare the efficiency of our MEBN model with that of a simple Bayesian network and demonstrate its superiority in cases that demand for situation-specific treatment.  a0302-9743