Since scientific literature is growing at a rapid rate and researchers today are faced with this publications deluge, it is increasingly tedious, if not practically impossible to keep up with the research progress even within one's own narrow discipline. The Open Research Knowledge Graph (ORKG) is posited as a solution to the problem of keeping track of research progress minus the cognitive overload that reading dozens of full papers impose. It aims to build a comprehensive knowledge graph that publishes th
NLPContributionGraph is defined on a dataset of NLP scholarly articles with their contributions structured to be integrable within Knowledge Graph infrastructures such as the ORKG . The structured contribution annotations are provided as: Contribution sentences : a set of sentences about the contribution in the article; Scientific terms and relations : a set of scientific terms and relational cue phrases extracted from the contribution sentences; and Triples : semantic statements that pair scientific terms
Thus NLPContributionGraph defines a three-element information extraction task for: 1) contribution sentences, 2) scientific term and predicate phrases from the sentences, and 3) triples formed using the extracted phrases under three (mandatory) or more information units.