4 research outputs found
Loki : the semantic wiki for collaborative knowledge engineering
We present Loki, a semantic wiki designed to support the collaborative knowledge engineering process with the use of software engineering methods. Designed as a set of DokuWiki plug-ins, it provides a variety of knowledge representation methods, including semantic annotations, Prolog clauses, and business processes and rules oriented to specific tasks. Knowledge stored in Loki can be retrieved via SPARQL queries, in-line Semantic MediaWiki-like queries, or Prolog goals. Loki includes a number of useful features for a group of experts and knowledge engineers developing the wiki, such as knowledge visualization, ontology storage, or code hint and completion mechanism. Reasoning unit tests are also introduced to validate knowledge quality. The paper is complemented by the formulation of the collaborative knowledge engineering process and the description of experiments performed during Loki development to evaluate its functionality. Loki is available as free software at https://loki.re
Checking and improving business process models in BPMN2
Business Process Modeling (BPM) is a systems engineering activity where we rep- resent the processes of an enterprise, so they can be shared, understood and improved. Despite the set of innovative tools for BPM modelling that exist in the market, they allow modelers to introduce errors during the modelling process. As there is no idea which errors the tools do not detect, what are the most recurrent errors and how could this prob- lem be mitigated, this dissertation presents a study and a proposal to help solving this problem. Firstly, a tool survey was developed to describe the state of the practice on the ability of Modelling Tools to validate BPMN2 models and determine the most recurrent defects introduced by BPMN modellers. Secondly, based on an empirical study using the QUASAR validator we provide evidence on its ability to validate a set of well-formedness rules and best practices and therefore detect errors in BPMN2 Models. Finally, we want to understand if this metamodelling-based validation facility can be used to prevent intro- ducing modelling errors, while speeding up the learning curve.A Modelação de Processos de Negócio (MPN) é uma atividade de engenharia de sistemas onde representamos os processos de uma empresa, para que os mesmos possam ser partilhados, compreendidos e melhorados. Apesar do elevado número de ferramentas de MPN existentes no mercado, estas permitem aos modeladores introduzir erros du- rante o processo de modelação. Como não existe uma ideia clara acerca de quais os erros que as ferramentas não detetam, quais os erros cometidos mais recorrentemente e como o problema pode ser resolvido, esta dissertação apresenta um estudo e uma proposta para resolver o problema. Inicialmente foi efetuado um levantamento do estado da prática da capacidade das ferramentas de modelação para validar os modelos em BPMN2, e determinar os erros mais frequentemente introduzidos pelos modeladores. Em seguida, baseado num estudo empírico, usando o validador QUASAR, fornecemos evidências sobre a sua capacidade para validar o conjunto de regras de boa formação e boas práticas na modelação de processos de negócio e assim detetar os erros introduzidos nos modelos em BPMN2. Finalmente, queremos compreender se esta facilidade de validação baseada em metamodelos pode ser usada para prevenir a introdução de erros durante o processo de modelação de processos de negócio, acelerando assim a curva de aprendizagem do modelador
at the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011)
Technical Report TR-2011/1, Department of Languages and Computation. University of Almeria November 2011. Joaquín Cañadas, Grzegorz J. Nalepa, Joachim Baumeister (Editors)The seventh workshop on Knowledge Engineering and Software Engineering (KESE7) was held at the Conference of the Spanish Association for Artificial Intelligence (CAEPIA-2011) in La Laguna (Tenerife), Spain, and brought together researchers and practitioners from both fields of software engineering and artificial intelligence. The intention was to give ample space for exchanging latest research results as well as knowledge about practical experience.University of Almería, Almería, Spain. AGH University of Science and Technology, Kraków, Poland. University of Würzburg, Würzburg, Germany