Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

Muhidin Mohamed, Mourad Oussalah

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalInternational Journal of Advanced Computer Science and Applications
Issue number7
Publication statusPublished - 1 Jul 2014

Bibliographical note

This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.


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