Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better results than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of $0.93$. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.

In this post, we summarise the final two invited talks from the International Conference on Machine Learning (ICML 2022). These presentations covered estimation and inference for causal effects, and machine learning for drug discovery and development. Guido's talk covered the topic of estimation and inference for causal effects in panel data settings, in particular focussing on synthetic control methods and difference-in-difference methods. These methods are very popular in the empirical literature in economics, but many questions remain concerning causal effects in these settings. There has been a lot of recent theoretical work trying to improve practices in this field.

Static analysis is a core task in query optimization and knowledge base verification. We study static analysis techniques for SPARQL, the standard language for querying Semantic Web data. Specifically, we investigate the query containment problem and query-update independence analysis. We are interested in developing techniques through reductions to the validity problem in logic.

The future both thrills and frightens us. Mark Shakr revealed its self-driving bar, which combines an autonomous vehicle with robotics capable of serving up the finest adult beverages. No matter where a thirsty soul stands, the Makr Shakr "Guido," as the company calls it, can serve up a drink on the spot. The concept envisions users placing and order and paying for their drink of choice via an app and Guido will mix it up and deliver the drink. No human interaction is required at all.