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Last update: RNDr. Michal Kopecký, Ph.D. (09.05.2019)
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Last update: prof. RNDr. Peter Vojtáš, DrSc. (12.10.2017)
Terms of passing the course consist of reporting on current achievements, induction on semantized data, project of a virtual Lean Startup and customer imitation via a social network. These are only conditions for getting credits. Exam is oral and requires basic understanding of whole material. As soon as terminology is introduced, detailed milestones (also form of deliverables) and preferred deadlines will be announced (with possible repeated attempts). There is no evidence on personal presence. Nevertheless, no additional explanation for tasks will be given, except on the respective lab and brief description on the course web. Final deadline is end of semester (without repeated attempts). |
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Last update: RNDr. Michal Kopecký, Ph.D. (10.05.2017)
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Last update: RNDr. Michal Kopecký, Ph.D. (10.05.2017)
Basic problems and vision of automation of web content processing, extraction, annotation Lean start-up methodology and semantization RDF-framework, description logic, OWLData model RDF and RDFS as a model of metadata, formal semantics, satisfiability Basics of description logic (DeL), knowledge and ontology representation Web querying languagesLanguage SPARQL, SPARQL algebra Dynamic logic Propositional dynamic logic (DyL) Decidability of DyL A dynamic model of web semantizationIntegration of W3C models and Dynamic logic Reliability of automated web information extraction and annotation A Kripke style model: states are query_based_predicate logic, programs (extractors) remain propositional + information on training extractors (metrics, data) A Hypothesis - Extraction success is similar on similar resources (e.g. created by same templates) |