The Department for Data, Knowledge and Process Management is pleased to announce the following talk by Prof. Heiko Paulheim from University of Mannheim:  

New Adventures in RDF2vec

Knowledge graphs are large-scale collections of knowledge of one or more domains, which can be consumed both by humans and computers. For exploiting knowledge graphs in systems using machine learning, they typically need to be transformed to a propositional, i.e., vector-shaped representation of entities. RDF2vec is an example for generating such vectors from knowledge graphs, relying on random walks for extracting pseudo-sentences from a graph, and utilizing word2vec for creating embedding vectors from those pseudo-sentences. In this talk, I will give insights into the idea of RDF2vec, possible application areas, and recently developed variants incorporating different walk strategies and training variations. Moreover, I will step away from purely quantitative evaluations and take a deeper look at what knowledge graph embedding methods like RDF2vec are generally capable of learning.

Short bio: Heiko Paulheim is a Full Professor for Data Science at the University of Mannheim. His group conducts various projects around knowledge graphs, yielding, among others, the public knowledge graphs WebIsALOD, CaLiGraph, and DBkWik. Moreover, his group is concerned with using knowledge graphs in machine learning, which has lead to the development of the widespread RDF2vec method for knowledge graph embeddings. In the recent past, Heiko Paulheim also leads projects which are concerned with ethical, societal, and legal aspects of AI, including KareKoKI, which deals with the impact of price-setting AIs on antitrust legislation, and the ReNewRS project on ethical news recommenders.

Date: Friday, 2nd September, 1pm

Location: D2.0.030