A key challenge in the semantic web is the mapping between different concepts. Many techniques for such mapping exist, but most of them induce a one-to-one mapping, which does not seem to correspond to real world problems. This project proposes a new approach, which tries to use the power of machine learning, and in particular classification algorithms, to solve the mapping task. It introduces ...

Semantic 3D mapping is one of the most important fields in robotics, and has been used in many applications, such as robot navigation, surveillance, and virtual reality. In general, semantic 3D mapping is mainly composed of 3D reconstruction and semantic segmentation. As these technologies evolve, there has been great progress in semantic 3D mapping in recent years. Furthermore, the number of r...

Many of the most important learning goals can only be achieved over several years. This means that each semester’s teaching should build these skills. Our CUSP system helps achieve this over the 3 5 years of a university degree: it enables each teacher to map their own subject design to institutional learning goals; it creates both subject and degree-level models. It tackles the semantic mappin...

In text management tasks, the dimensionality reduction becomes necessary to computation and interpretability of the results generated by machine learning algorithms. This paper describes a feature extraction method called semantic mapping. Semantic mapping, sparse random mapping and PCA are applied to self-organization of document collections using self-organizing map (SOM). The behaviors of th...