Probabilistic logical learning for biclustering: A case study with surprising results

Many approaches to probabilistic logical learning have been proposed by now, and several of these have been implemented into powerful learning and inference systems. Given this state of the art, it appears natural to start using these systems for solving concrete problems. This paper presents some results of a case study where several probabilistic logical learning systems have been applied to a seemingly simple problem that exhibits both probabilistic and relational aspects. The results are surprisingly negative: none of the systems we have tried could adequately handle the problem at hand. We discuss the reasons for this. This leads to several conclusions. First, still more effort must be invested in developing full-fledged implementations that can handle a wide range of realistic problems. Second, the intrinsic limitations of certain approaches may not yet be fully understood. Third, the problem we discuss here may be an interesting application for probabilistic logical learning systems, and we invite other researchers to use it as a benchmark for evaluating the applicability of their favorite systems.nrpages: 8status: publishe