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- W2951170426 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." @default.
- W2951170426 created "2019-06-27" @default.
- W2951170426 creator A5032607630 @default.
- W2951170426 date "2018-11-06" @default.
- W2951170426 modified "2023-09-24" @default.
- W2951170426 title "Parser Extraction of Triples in Unstructured Text" @default.
- W2951170426 doi "https://doi.org/10.11591/ij-ai.v5.i4.pp143-148" @default.
- W2951170426 hasPublicationYear "2018" @default.
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