Probabilistic Logic Learning Luc De Raedt Kristian Kersting Institut fur¨ Informatik, Albert-Ludwigs-University, Institut fur¨ Informatik, Albert-Ludwigs-University, Georges-Koehler-Allee, Building 079, D-79110 Georges-Koehler-Allee, Building 079, D-79110 Freiburg, Germany Freiburg, Germany
[email protected] [email protected] ABSTRACT The past few years have witnessed an significant interest in probabilistic logic learning, i.e. in research lying at the in- Probability Logic tersection of probabilistic reasoning, logical representations, and machine learning. A rich variety of different formalisms and learning techniques have been developed. This paper Probabilistic provides an introductory survey and overview of the state- Logic Learning Learning of-the-art in probabilistic logic learning through the identi- fication of a number of important probabilistic, logical and learning concepts. Figure 1: Probabilistic Logic Learning as the intersection of Keywords Probability, Logic, and Learning. data mining, machine learning, inductive logic program- ing, diagnostic and troubleshooting, information retrieval, ming, multi-relational data mining, uncertainty, probabilis- software debugging, data mining and user modelling. tic reasoning The term logic in the present overview refers to first order logical and relational representations such as those studied 1. INTRODUCTION within the field of computational logic. The primary advan- One of the central open questions in data mining and ar- tage of using first order logic or relational representations tificial intelligence, concerns probabilistic logic learning, i.e. is that it allows one to elegantly represent complex situa- the integration of relational or logical representations, prob- tions involving a variety of objects as well as relations be- abilistic reasoning mechanisms with machine learning and tween the objects.