  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.
