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Ensuring the Semantic Correctness of a BAUML Artifact-centric BPM (Summary)

Using models to represent business processes provides several advantages, such as being able to check the correctness of the processes before their implementation. In contrast to traditional process modeling approaches, the artifact-centric approach treats data as a key element of the process, also considering the tasks or activities that are performed in it. This paper presents a way to verify and validate the semantic correctness of an artifact-centric business process model defined using a combination of UML and OCL models – a BAUML model. To do this, we provide a method to translate all BAUML components into a set of logic formulas. The result of this translation ensures that the only changes allowed are those specified in the model, and that those changes are taking place according the order established by the model. Having obtained this logic representation, these models can be validated by any existing reasoning method able to deal with negation of derived predicates. Moreover, we show how to automatically generate the relevant tests to validate the models and we prove the feasibility of our approach.

AYNEC-DataGen: a tool for generating evaluation datasets for Knowledge Graphs completion

In the context of knowledge graphs, the task of completion of relations consists in adding missing triples to a knowledge graph, usually by classifying potential candidates as true of false. Creating an evaluation dataset for these techniques is not trivial, since there is a large amount of variables to consider which, if not taken into account, may cause misleading results. So far, there is not a well defined workflow that identifies the variation points when creating a dataset, and what are the possible strategies that can be followed in each step. Furthermore, there are no tools that help create such datasets in an easy way. To address this need, we have created AYNEC-DataGen, a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of the original knowledge graph, the splitting of triples into training and testing sets, and the generation of negative examples. The output of our tool includes the evaluation dataset, an optional export in an open format for its visualisation, and additional files with metadata. Our tool is freely available online.