Knowledge base population and visualization using an ontology based on semantic roles
|Knowledge base population and visualization using an ontology based on semantic roles|
|Author(s)||Siahbani M., Vadlapudi R., Whitney M., Sarkar A.|
|Published in||AKBC 2013 - Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, Co-located with CIKM 2013|
|Keyword(s)||information extraction, knowledge base, ontology (Extra: Information visualization system, Knowledge base, Natural language text, Natural languages, Ontology-based, Semantic roles, Wikipedia, Data mining, Information retrieval, Ontology, Semantics, Knowledge based systems)|
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Knowledge base population and visualization using an ontology based on semantic roles is a 2013 conference paper written in English by Siahbani M., Vadlapudi R., Whitney M., Sarkar A. and published in AKBC 2013 - Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, Co-located with CIKM 2013.
This paper extracts facts using "micro-reading" of text in contrast 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 find 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.
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