Abstract. One of the main problems when creating execution-level process models is finding implementations for process activities. Carrying out this activity manually can be time consuming, since it involves searching in large service repositories. We present Maestro for BPMN, a tool that allows to annotate and automatically compose activities within business processes. We explain the main assumptions and algorithms underlying the tool, and we overview what will be demonstrated at ESWC.

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The task of unsupervised induction of probabilistic context-free grammars (PCFGs) has attracted a lot of attention in the field of computational linguistics.Although it is a difficult task, work in this area is still very much in demand since it can contribute to the advancement of language parsing and modelling. In this work, we describe a new algorithm for PCFG induction based on a principled approach and capable of inducing accurate yet compact artificial natural language grammars and typical context-free grammars. Moreover, this algorithm can work on large grammars and datasets and infers correctly even from small samples. Our analysis shows that the type of grammars induced by our algorithm are, in theory, capable of modelling natural language. One of our experiments shows that our algorithm can potentially outperform the state-of-the-art in unsupervised parsing on the WSJ10 corpus.

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