BET: An inductive logic programming workbench. Existing ILP (Inductive Logic Programming) systems are implemented in different languages namely C, Progol, etc. Also, each system has its customized format for the input data. This makes it very tedious and time consuming on the part of a user to utilize such a system for experimental purposes as it demands a thorough understanding of that system and its input specification. In the spirit of Weka [1], we present a relational learning workbench called BET (Background + Examples = Theories), implemented in Java. The objective of BET is to shorten the learning curve of users (including novices) and to facilitate speedy development of new relational learning systems as well as quick integration of existing ILP systems. The standardized input format makes it easier to experiment with different relational learning algorithms on a common dataset.

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