ELOG Reasoner

elog logo

Welcome to the online interface of the probabilistic (or more precise log-linear) description logic solver ELog. Log-linear description logics are a family of probabilistic logics integrating various concepts and methods from the areas of knowledge representation and reasoning and statistical relational AI. In ELog users can define both hard and soft axioms and compute marginal inference as well as the most probable coherent ontology. Please refer also to http://code.google.com/p/elog-reasoner/ for details.

The theoretical foundations of the reasoner and some experimental results are presented in the paper "Log-Linear Description Logics" (accepted for presentation at IJCAI 2011; download here (Bibtex))


Syntax and Solving Options

We provide:

Please refer to http://code.google.com/p/elog-reasoner/ for the source code, binaries, and installation instructions.

Your Feedback

If you use ELOG in some way we would be happy to hear about it. We would also be happy if you could send us a copy of any published work that uses ELOG. If you use ELOG in your research, please cite us!

The original Description Logic logo was designed by Enrico Franconi. We altered it with his permission to reflect the combination of description logics and log-linear models.

Active Support

We offer active support. You get support by either writing a mail to jan.noessner@gmail.com or by posting issues in the Issue Tracker. The second possibility is preferred. Support does also include help with defining models or enhancement wishes for rockIt. So if you have any problems or questions concerning rockIt just ask!

About This Service

Please note that we restricted the time of the ILP solver to 30 Minutes per cutting plane iteration in this online version. After 30 minutes the solver will return the best solution which it currently found. The maximal overall runtime is 3 hours per request. If your model requires more than three hours to be solved, the execution is cancelled.

This service is run on a Dual Core virtual machine with 8 GB ram. Please note that due to resource restrictions we run more than this service on the same machine. Thus, runtimes might vary.