This paper proposes a Unified Dynamic Relation Tree (DRT) span for tree kernel-based semantic relation extraction between entity names. The basic idea is to apply a variety of lin...
Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This ...
This work deals with the application of kernel methods to structured relational settings such as semantic knowledge bases expressed in Description Logics. Our method integrates a n...
Abstract. Combining statistical and relational learning receives currently a lot of attention. The majority of statistical relational learning approaches focus on density estimatio...
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...