Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content
Abstract
In 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.
Cite
Text
Chantas et al. "Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content." European Conference on Computer Vision Workshops, 2014. doi:10.1007/978-3-319-16181-5_25Markdown
[Chantas et al. "Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content." European Conference on Computer Vision Workshops, 2014.](https://mlanthology.org/eccvw/2014/chantas2014eccvw-multientity/) doi:10.1007/978-3-319-16181-5_25BibTeX
@inproceedings{chantas2014eccvw-multientity,
title = {{Multi-Entity Bayesian Networks for Knowledge-Driven Analysis of ICH Content}},
author = {Chantas, Giannis K. and Kitsikidis, Alexandros and Nikolopoulos, Spiros and Dimitropoulos, Kosmas and Douka, Stella and Kompatsiaris, Ioannis and Grammalidis, Nikos},
booktitle = {European Conference on Computer Vision Workshops},
year = {2014},
pages = {355-369},
doi = {10.1007/978-3-319-16181-5_25},
url = {https://mlanthology.org/eccvw/2014/chantas2014eccvw-multientity/}
}