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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Shaun D'Souza
CTO Office, Wipro
Computer Science & IT Engineering

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Parser Extraction of Triples in Unstructured Text Shaun D'Souza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (133.152 KB) | DOI: 10.11591/ijai.v5.i4.pp143-148

Abstract

The web contains vast repositories of unstructured text. We investigate the opportunity for building a knowledge graph from these text sources. We generate a set of triples which can be used in knowledge gathering and integration. We define the architecture of a language compiler for processing subject-predicate-object triples using the OpenNLP parser. We implement a depth-first search traversal on the POS tagged syntactic tree appending predicate and object information. A parser enables higher precision and higher recall extractions of syntactic relationships across conjunction boundaries. We are able to extract 2-2.5 times the correct extractions of ReVerb. The extractions are used in a variety of semantic web applications and question answering. We verify extraction of 50,000 triples on the ClueWeb dataset.
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