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arXiv · 1811.05768

Parser Extraction of Triples in Unstructured Text

Shaun D'Souza
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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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BibTeXRIS

Shaun D'Souza. 2018-11-06. Parser Extraction of Triples in Unstructured Text. https://doi.org/10.11591/ij-ai.v5.i4.pp143-148

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