Data from: Parser extraction of triples in unstructured text

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.