AKSW Colloquium, 04-05-2015, Automating RDF Dataset Transformation and Enrichment, Structured Machine Learning in Life Science

Automating RDF Dataset Transformation and Enrichment by Mohamed Sherif

Mohamed SherifWith the adoption of RDF across several domains, come growing requirements pertaining to the completeness and quality of RDF datasets. Currently, this problem is most commonly addressed by manually devising means of enriching an input dataset. The few tools that aim at supporting this endeavour usually focus on supporting the manual definition of enrichment pipelines. In this talk, we present a supervised learning approach based on a refinement operator for enriching RDF datasets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against eight manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples.

Structured Machine Learning in Life Science (PhD progress report) by Patrick Westphal

Patrick WestphalThe utilization of machine learning techniques to solve life science tasks has become widespread within the last years. Mainly working on unstructured data one question is whether such techniques could benefit from the provision of structured background knowledge. One prevalent way to express background knowledge in the life sciences is the Web Ontology Language (OWL). Accordingly there is a great variety of different domain ontologies covering anatomy, genetics, biological processes or chemistry that can be used to form structured machine learning approaches in the life science domain. The talk will give a brief overview of tasks and problems of structured machine learning in life science. Besides the special characteristics observed when applying the state-of-the-art concept learning approaches to life science tasks, a short description of the actual differences to concept learning setups in other domains is given. Further, some directions for machine learning based techniques are shown that could support concept learning in life science tasks.

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.

This entry was posted in Uncategorized. Bookmark the permalink.