Kelvin: Extracting Knowledge from Large Text Collections

September 8th, 2014

Preprint: James Mayfield, Paul McNamee, Craig Harman, Tim Finin and Dawn Lawrie, KELVIN: Extracting Knowledge from Large Text Collections, AAAI Fall Symposium on Natural Language Access to Big Data, 2014.

We describe the \kelvin system for extracting entities and relations from large text collections and its use in the TAC Knowledge Base Population Cold Start task run by the U.S. National Institute of Standards and Technology. The Cold Start task starts with an empty knowledge based defined by an ontology or entity types, properties and relations. Evaluations in 2012 and 2013 were done using a collection of text from local Web and news to de-emphasize the linking entities to a background knowledge bases such as Wikipedia. Interesting features of \kelvin include a cross-document entity coreference module based on entity mentions, removal of suspect intra-document conference chains, a slot value consolidator for entities, the application of inference rules to expand the number of asserted facts and a set of analysis and browsing tools supporting development.