Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system’s outstanding performance in comparison to three different state-of-the-art systems.

@article{Mohamed2014,
  title     = {Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {7},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Muhidin Mohamed and Mourad Oussalah},
  doi       = {10.14569/IJACSA.2014.050725},
  url       = {https://doi.org/10.14569/IJACSA.2014.050725}
}