TY - JOUR
T1 - Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints
AU - Santos, Eugene
AU - Li, Deqing
AU - Santos, Eunice E.
AU - Korah, John
N1 - Funding Information:
This work was supported in part by a grant from the Air Force Research Laboratory, Information Directorate, Office of Naval Research Grant No. N00014-06-C-0020 , and Air Force Office of Scientific Research Grant Nos. FA9550-06-1-0169 , FA9550-07-1-0050 and FA9550-09-1-0716 , and Defense Threat Reduction Agency Grant No. HDTRA1-10-1-0096 . A preliminary version of this paper can be found in ( Santos et al., 2009a ).
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.
AB - Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.
KW - Bayesian Knowledge-Base
KW - Knowledge representation
KW - Probabilistic reasoning
KW - Temporal reasoning
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U2 - 10.1016/j.eswa.2012.05.002
DO - 10.1016/j.eswa.2012.05.002
M3 - Article
AN - SCOPUS:84865039029
SN - 0957-4174
VL - 39
SP - 12905
EP - 12917
JO - Expert Systems With Applications
JF - Expert Systems With Applications
IS - 17
ER -