Abstract
In this paper a knowledge base concept driven named entity recognition (NER) approach is presented. The technique is used for information extraction from news articles and linking it with background concepts in knowledge base. The work specifically focuses on extracting entity mentions from unstructured articles. The extraction of entity mentions from articles is based on the existing concepts from DBPedia ontology, representing the knowledge associated with the concepts present in Wikipedia knowledge base. A collection of the Wikipedia concepts through structured DBpedia ontology has been extracted and developed. For processing of unstructured text, Dawn news articles have been scrapped, preprocessed and thereby a corpus has been built. The proposed knowledge base driven system shows that given an article, the system identifies the entity mentions in the text article and how they can automatically be linked with the concepts to the corresponding entity mentions representing their respective pages on Wikipedia. The system is evaluated on three test collections of news articles on politics, sports and entertainment domains. The experimental results in respect of entity mentions are reported. The results are presented as precision, recall and f-measure, where the precision of extraction of relevant entity mentions identified yields the best results with a little variation in percent recall and f-measures. Additionally, facts associated with the extracted entity mentions both in form of sentences and Resource Description Framework (RDF) triples are presented so as to enhance the user’s understanding of the related facts presented in the article.
Highlights
The text contained in unstructured documents, such as news articles or scientific literature, is often replete with many different persons, organizations, places, time, spatial information, etc
The Wikipedia concepts representing three different set of persons from Pakistan was collected using existing DBpedia ontology classes through OpenLink Virtuoso simple protocol and RDF Query Language (SPARQL) endpoint and tested the same over the Dawn news article corpus across three domainspecific news articles Pakistan, Sports and Entertainment
All in all the proposed technique resulted in 100% precision, that is, all entity mentions were correctly identified as persons the recall varied from 20% to 60%, suggesting that some of the entity mentions were present in the articles they could not be identified
Summary
The text contained in unstructured documents, such as news articles or scientific literature, is often replete with many different persons, organizations, places, time, spatial information, etc These relevant subjects, generally referred to as entity mentions in unstructured text are cited in form of words or phrases. The entity mentions are representative of names such as persons, organizations, places, date, time, locations, etc It is one of the subtasks associated with information extraction which helps identify mentions to its one of known categories or classes as mentioned previously.
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