**Machine Learning and the Semantic Web** [Claudia d'Amato](http://www.di.uniba.it/~cdamato) and [Nicola Fanizzi](http://www.di.uniba.it/~fanizzi) [Scuola di Dottorato in Informatica e Matematica](http://dottorato.di.uniba.it) -- XXXIII Cycle [Università degli studi di Bari](http://www.uniba.it) "Aldo Moro" (#) Course __Objectives__ Exploring the intersections of _Machine Learning_ with the _Semantic Web_ / _Web of Data_, regarded as a major source for Big Data. (#) Lectures (May 2018) + Fri 11: [Introduction to the Semantic Web](./MLSW-Intro.pdf) + Wed 16: [Representation Languages for the Semantic Web](MLSW-Langs.pdf) * RDF, RDF-S, OWL + Fri 18: [Languages](MLSW-Langs.pdf) / [Relational Learning](MLSW-RelationalLearning.pdf) * OWL/DL * DM/ML Problems: Inductive Classification + Fri 25: [Reasoning](#) / [Concept Learning](MLSW-ConceptLearning.pdf): * DL Tableaux * LCS, DL-Foil, Terminological Decision Trees, [DL-Learner] + Mon 28: [ML Problems](MLSW-KM-EBEM.pdf): * Kernel Methods, Energy-based Embedding Models + Tue 29: [ML methods for Semantic Web Problems](MLSW-ML4SW.pdf) * inductive retrieval/classification, ontology enrichment etc. [^tbd]: to be decided. (##) Material + Semantic Web at [W3C](https://www.w3.org/standards/semanticweb/) * [RDF](https://www.w3.org/RDF/), [RDF-Schema](https://www.w3.org/TR/rdf-schema/) * [OWL](https://www.w3.org/TR/owl2-overview/)2 + [Linked Data](http://linkeddata.org/) + [Linked Open Vocabularies](http://lov.okfn.org/dataset/lov/) + Machine Learning textbooks (for further reading/insights) + DeRaedt: [Logical and Relational Learning](#) + Mitchell: Machine Learning + MacKay: [Information Theory, Inference, and Learning Algorithms](http://www.inference.org.uk/itprnn/book.html) - Hastie et al.: [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf) (#) Exam Presentation of a (draft of a) solution to an assigned problem of student's choice. [List](MLSW-Projects.pdf) of proposed problems. ----