Jędrzej Potoniec

We present a novel approach for learning embeddings of ALC knowledge base concepts. The embeddings reflect the semantics of the concepts in such a way that it is possible to compute an embedding of a complex concept from the embeddings of its parts by using appropriate neural constructors. Embeddings for different knowledge bases are vectors in a shared vector space, shaped in such a way that approximate subsumption checking for arbitrarily complex concepts can be done by the same neural network, called a reasoner head, for all the knowledge bases. To underline this unique property of enabling reasoning directly on embeddings, we call them reason-able embeddings. We report the results of experimental evaluation showing that the difference in reasoning performance between training a separate reasoner head for each ontology and using a shared reasoner head, is negligible.

Capturing business process specifics using a model is essential to effectively manage, control, and instruct the process participants with their roles and tasks. A normative process model is an invaluable source of information, not only for human inspection but also for software supporting and controlling the process. The actual process execution likely deviates from the normative model and the magnitude of deviation usually increases over time due to process evolution. A descriptive process model is far more informative when comes to the analysis of the actual execution of the process and spotting the deviations from the norm. However, handcrafting the descriptive model using process-related documents is prohibitively laborious. To automate the discovery of the descriptive process models, we propose Online Miner (OM), an algorithm that continuously builds process models using a stream of events raised by the process. OM first builds a sound process model using a historical event log and then incrementally adapts this model to new data chunks. OM represents the model using a Causal-net – a dependency graph of activities in which arcs are bound with each other to reflect parallel dependencies and exclusive alternatives. OM finds the maximal bindings being consistent with the event log. OM produces sound and perfect fit and generalizing well models. OM quickly reacts to the concept drift spot in the event log by seamlessly adapting the model. OM helps business analytics experts with their everyday tasks of monitoring, auditing, and enhancing business processes.