Knowledge Base Population and Visualization Using an Ontology based on Semantic Roles

Maryam Siahbani, Ravikiran Vadlapudi, Max Whitney, Anoop Sarkar

Jun 28, 2013 (modified: Jun 28, 2013) AKBC 2013 submission readers: everyone
  • Abstract: This paper extracts facts using 'micro-reading' of text in constrast to approaches that extract common-sense knowledge using 'macro-reading' methods. Our goal is to extract detailed facts about events from natural language using a predicate-centered view of events (who did what to whom, when and how). We exploit semantic role labels in order to create a novel predicate-centric ontology for entities in our knowledge base. This allows users to nd uncommon facts easily. To this end, we tightly couple our knowledge base and ontology to an information visualization system that can be used to explore and navigate events extracted from a large natural language text collection. We use our methodology to create a web-based visual browser of history events in Wikipedia.
  • Decision: conferencePoster
  • Authorids: msiahban@sfu.ca, rvadlapu@sfu.ca, mwhitney@sfu.ca, anoop.sarkar@gmail.com

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