We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, r...
In this paper, a region-based spatio-temporal Markov random field (STMRF) model is proposed to segment moving objects semantically. The STMRF model combines segmentation results o...
Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques...
The field of Semantic Business Process Management (SBPM) has refuelled interest in using ontologies for the representation of the static and dynamic aspects of an enterprise and va...
Agata Filipowska, Martin Hepp, Monika Kaczmarek, I...
Abstract. When creating execution-level process models from conceptual to-be process models, challenges are to find implementations for process activities and to use these impleme...