Populating the Semantic Web by Macro-Reading Internet Text T.M Mitchell, J. Betteridge, A. Carlson, E. Hruschka, R. Wang Presented by: Will Darby

Problem Semantic web offers many promises: – Standardized ontologies – Vast machine readable data repositories – Intelligent agents Internet currently contains unstructured documents – Mark up used for visual presentation – Little or no valuable metadata How to migrate WWW to Semantic Web – Author future documents with explicit ontologies? – Publish existing databases with semantic web services? – Augment existing Internet data with automatically extracted semantics?

Approach Initially, extract common, redundant data “Macro-Read” Internet – Extract most prevalent facts from large text collection – Natural language processing to identify simple facts – Statistically combine evidence to select most likely information Ontology based analysis – Focus on relevant subset of text corpus – Ontology defines categories and relations to guide learning Semi-supervised machine learning – Bootstrapped from seed examples corresponding to Ontology – Separate learning systems for HTML and text – Increase in Ontology complexity results in higher accuracies