Abstract
It has become common to use RDF to store the results of Natural Language Processing (NLP) as a graph of the entities mentioned in the text with the relationships mentioned in the text as links between them. These NLP graphs can be measured with Precision and Recall against a ground truth graph representing what the documents actually say. When asking conjunctive queries on NLP graphs, the Recall of the query is expected to be roughly the product of the Recall of the relations in each conjunct. Since Recall is typically less than one, conjunctive query Recall on NLP graphs degrades geometrically with the number of conjuncts. We present an approach to address this Recall problem by hypothesizing links in the graph that would improve query Recall, and then attempting to find more evidence to support them. Using this approach, we confirm that in the context of answering queries over NLP graphs, we can use lower confidence results from NLP components if they complete a query result.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
