KELVIN: Extracting knowledge from large text collections

James Mayfield, Paul McNamee, Craig Harman, Tim Finin, Dawn Lawrie

Abstract:

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 of 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 use of entities that
appear in a background knowledge base 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