Survey of Multi Entity Bayesian Network (MEBN) and its applications in probabilistic reasoning.
Main Article Content
Abstract
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
Wang, Pei. "The limitation of Bayesianism." Artificial Intelligence 158.1 (2004): 97-106.
K. B. Laskey, “MEBN: A language for first-order Bayesian knowledge bases,†Artif. Intell., vol. 172, no. 2–3, pp. 140–178, 2008.
L. Getoor and B. Taskar, Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press, 2007.
Howard, Catherine, and Markus Stumptner. "A Survey of Directed Entity-Relation--Based First-Order Probabilistic Languages." ACM Computing Surveys (CSUR) 47.1 (2014): 4
Da Costa, Paulo Cesar G., Kathryn B. Laskey, and Kenneth J. Laskey. "Pr-owl: A bayesian ontology language for the semantic web." Proceedings of the 2005 International Conference on Uncertainty Reasoning for the Semantic Web-Volume 173. CEUR-WS. org, 2005.
Carvalho, Rommel N., Kathryn B. Laskey, and Paulo CG Costa. "PR-OWL 2.0-bridging the gap to OWL semantics." Proceedings of the 6th International Conference on Uncertainty Reasoning for the Semantic Web-Volume 654. CEUR-WS. org, 2010.
G. A. AlGhamdi, K. B. Laskey, E. J. Wright, D. Barbara, and K. Chang, “Modeling insider user behavior using multi-entity Bayesian network,†in 10th International Command and Control Research and Technology Symposium, 2008, vol. 4444, no. 703.
D. Jain, L. Mosenlechner, and M. Beetz, “Equipping robot control programs with first-order probabilistic reasoning capabilities,†2009 IEEE Int. Conf. Robot. Autom., pp. 3626–3631, 2009.
R. N. Carvalho, P. C. G. Costa, K. B. Laskey, and K. C. Chang, “PROGNOS: Predictive situational awareness with probabilistic ontologies,†2010 13th Int. Conf. Inf. Fusion, pp. 1–8, 2010.
R. N. Carvalho, R. Haberlin, P. C. G. Costa, K. B. Laskey, and K. C. Chang, “Modeling a probabilistic ontology for Maritime Domain Awareness,†14th Int. Conf. Inf. Fusion, pp. 1–8, 2011.
K. B. Laskey, P. C. G. Costa, and T. Janssen, “Probabilistic ontologies for multi-INT fusion,†Front. Artif. Intell. Appl., vol. 213, pp. 147–161, 2010.
M. D. Mas, “Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating,†CoRR, vol. abs/1009.2, pp. 1–20, 2010.
H. Bouhamed, A. Rebai, T. Lecroq, and M. Jaoua, “Data-organization before Learning Multi-Entity Bayesian Networks Structure,†pp. 305–308, 2011.
R. N. Carvalho, M. Ladeira, and L. Weigang, “Probabilistic Ontologies Incremental Modeling Using UnBBayes,†2011.
R. N. Carvalho, K. B. Laskey, and P. C. G. Da Costa, “Uncertainty modeling process for semantic technology,†PeerJ Comput. Sci., vol. 2, p. e77, 2016.
C. Ewell, “Detection of Deviations From Authorized Network Activity Using Dynamic Bayesian Networks,†2011.
A. Boruah, “A Probabilistic Approach to Detect and Prevent Bandwidth Depletion Attacks,†Int. J. Comput. Appl., vol. 150, no. 5, pp. 42–49, 2016.
Z. Yun, Z. Cheng, L. Ting, Z. Weiming, and L. Zhong, “A COG Analysis Model of System-of-Systems ( SoS ) Based on Multi-Entity Bayesian Networks ( MEBN ),†2012.
Wang, Hao-Ran, et al. "Tactical Air Target Intention Recognition Based on Multi-Entities Bayesian Network." Huoli yu Zhihui Kongzhi 37.10 (2012): 133-138.
K. Golestan, F. Karray, and M. Kamel, “High level information fusion through a fuzzy extension to multi-entity bayesian networks in vehicular ad-hoc networks,†IEEE 16th Int. Conf. Inf. Fusion, pp. 1180–1187, 2013.
K. Golestan, R. Soua, F. Karray, and M. S. Kamel, “A model for situation and threat/impact assessment in vehicular ad-hoc networks,†Proc. fourth ACM Int. Symp. Dev. Anal. Intell. Veh. networks Appl. - DIVANet ’14, pp. 87–94, 2014.
K. Golestan, B. Khaleghi, F. Karray, and M. S. Kamel, “Attention Assist: A High-Level Information Fusion Framework for Situation and Threat Assessment in Vehicular Ad Hoc Networks,†IEEE Trans. Intell. Transp. Syst., vol. 17, no. 5, pp. 1271–1285, 2016.
C. Y. Park, K. B. Laskey, P. Costa, and S. Matsumoto, “Multi-Entity Bayesian Networks Learning In Predictive Situtation Awareness,†Proc. 18th …, vol. 4444, no. 703, pp. 1–20, 2013.
Cypko, M., et al. "User interaction with MEBNs for large patient-specific treatment decision models with an example for laryngeal cancer." Int J CARS 9.Suppl 1 (2014).
Sarkar, I. N. "Web-tool to Support Medical Experts in Probabilistic Modelling Using Large Bayesian Networks With an Example of Hinosinusitis." (2015).
O. Jules, A. Hafid, and M. A. Serhani, “Bayesian network, and probabilistic ontology driven trust model for SLA management of Cloud services,†2014 IEEE 3rd Int. Conf. Cloud Networking, CloudNet 2014, pp. 77–83, 2014.
G. Chantas, A. Kitsikidis, S. Nikolopoulos, K. Dimitropoulos, S. Douka, I. Kompatsiaris, and N. Grammalidis, “Multi-entity Bayesian networks for knowledge-driven analysis of ICH content,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8926, pp. 355–369, 2015.
T. Martin, K. C. Chang, X. Tian, G. Chen, T. Nguyen, K. D. Pham, and E. Blasch, “A probabilistic situational awareness and reasoning methodology for satellite communications resource management,†IEEE Aerosp. Conf. Proc., vol. 2015-June, pp. 1–12, 2015.
X. Tian, G. C. Ift, T. Martin, K. C. C. Gmu, T. N. Cua, K. Pham, and E. B. Afrl, “Multi-entity Bayesian network for the handling of uncertainties in SATCOM Reconfiguring SATCOM Resources,†vol. 9469, pp. 1–11, 2015.
L. L. Santos, R. N. Carvalho, M. Ladeira, and L. Weigang, “A new algorithm for generating situation-specific Bayesian networks using Bayes-Ball method,†CEUR Workshop Proc., vol. 1665, pp. 36–48, 2016.
https://sourceforge.net/projects/unbbayes/ (accessed 12th June 2017)