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Mining the Semantic Web: Requirements for Machine Learning Fabio Ciravegna, Sam Chapman Presented by Steve Hookway 10/20/05 What is the Semantic Web A way to automate reasoning with web data RDF A uniform way to describe resources (subject,predicate,object) Ontology Hierarchical structure of data Property restrictions Implicit typing Adding Meta-Data A prerequisite for Semantic Web (SW) is structured knowledge Manual Approach Too Much data Trust Issues Noise This process needs to be automated Armadillo Automatically annotate web pages Validity based on a number of weak techniques Redundant Information Rating of Sources Context around a capture (LP)² - Extraction of knowledge Makes use of Natural Language Processing (NLP) (LP)² Induce tagging rules Contextual Tagging Generalize NLP and keep best rules <tag> Remove covered instances from pool High Precision, Low Recall Recovers rules and constrains their application </tag> Correction and Validation Shifts tags to correct position (within d spaces) Validation Heterogeneity Armadillo Uses weak NLP Uses intra-document relation recognition Requirements Must adapt to different document types Relation Extraction Bootstrapping Learning Armadillo Unsupervised approach – user only validates User cannot drive system towards interesting documents and facts Requirements Identify triples Goal: Bootstrap learning on a large scale User needs a role to guide learning Content Cleaning and Normalization Armadillo Noise added during unsupervised (LP)² Use the multiple weak evidence to help avoid poor seeds Requirements Handle noisy training data Conclusion Semantic Web Armadillo – a tool for IE Meta-Data Evidence Building and Validation Extraction of knowledge (LP)² A survey of requirements in mining web content for SW meta-data