MEX – Publishing ML Experiment Results by Diego Esteves
Over the decades many machine learning experiments have been published, collaborating with the scientific community progress. One of the key-factors in order to compare machine learning experiment results to each other and collaborate positively is to thoroughly perform them on the same computing environment using the same sample data sets and algorithms configurations. Besides, practical experience shows that scientists and engineers tend to have too many output data for their sets of experiments, which, in the end, is either difficult to be analyzed without a provenance metadata as well as archive properly. Despite the efforts for publishing and managing these variables accordingly, we still have a knowledge gap, which is explicated by a missing public ontology for machine learning experiments in order to achieve the interoperability for published results. In order to minimize this gap, we introduce the novel MEX Ontology, built up based on the W3C PROV Ontology (PROV-O) and following the nanopublication principles.
Scaling DL-Learner – Status and Plans by Simon Bin
With the event of Big Data and Large Scale Data Processing, new challenges also are approaching the DL-Learner. The framework for supervised Machine Learning on OWL and RDF may benefit from various approaches to make it work better with huge data. In this talk, first experimental results of using SPARQL instead of traditional OWL-Reasoner approaches will be shown and possible future directions for scaling DL-Learner will be sketched.
About the AKSW Colloquium
This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/Colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.