@article{(Open Science Index):https://publications.waset.org/pdf/536,
	  title     = {Use of Bayesian Network in Information Extraction from Unstructured Data Sources},
	  author    = {Quratulain N. Rajput and  Sajjad Haider},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {4},
	  year      = {2009},
	  pages     = {950 - 956},
	  ee        = {https://publications.waset.org/pdf/536},
	  url   	= {https://publications.waset.org/vol/28},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 28, 2009},
	}