@article{(Open Science Index):https://publications.waset.org/pdf/10007303,
	  title     = {Evolving Knowledge Extraction from Online Resources},
	  author    = {Zhibo Xiao and  Tharini Nayanika de Silva and  Kezhi Mao},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, we present an evolving knowledge
extraction system named AKEOS (Automatic Knowledge Extraction
from Online Sources). AKEOS consists of two modules, including
a one-time learning module and an evolving learning module.
The one-time learning module takes in user input query, and
automatically harvests knowledge from online unstructured resources
in an unsupervised way. The output of the one-time learning is a
structured vector representing the harvested knowledge. The evolving
learning module automatically schedules and performs repeated
one-time learning to extract the newest information and track the
development of an event. In addition, the evolving learning module
summarizes the knowledge learned at different time points to produce
a final knowledge vector about the event. With the evolving learning,
we are able to visualize the key information of the event, discover
the trends, and track the development of an event.},
	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {11},
	  number    = {6},
	  year      = {2017},
	  pages     = {746 - 752},
	  ee        = {https://publications.waset.org/pdf/10007303},
	  url   	= {https://publications.waset.org/vol/126},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 126, 2017},