We are very pleased to announce that our group got two papers accepted for presentation at DEXA2020 (International Conference on Database and Expert Systems Applications). Since 1990, DEXA has been an annual international conference which showcases state-of-the-art research activities in database, information, and knowledge systems. DEXA provides a forum to present research results and to examine advanced applications in the field. The conference and its associated workshops offer an opportunity for developers, scientists, and users to extensively discuss requirements, problems, and solutions in database, information, and knowledge systems.
Here are the pre-print of the accepted papers with their abstract:
- Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
By Ariam Rivas, Irlán Grangel-González, Diego Collarana, Jens Lehmann, and Maria-Esther Vidal.
AbstractIndustry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans* family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
- SCODIS: Job Advert-derived Time Series for high-demand Skillset Discovery and Prediction
By Elisa Margareth Sibarani and Simon Scerri.
AbstractIn this paper, we consider a dataset compiled from online job adverts for consecutive ﬁxed periods, to identify whether repeated and automated observation of skills requested in the job market can be used to predict the relevance of skillsets and the predominance of skills in the near future. The data, consisting of co-occurring skills observed in job adverts, is used to generate a skills graph whose nodes are skills and whose edges denote the co-occurrence appearance. To better observe and interpret the evolution of this graph over a period of time, we investigate two clustering methods that can reduce the complexity of the graph. The best performing method, evaluated according to its modularity value (0.72 for the best method followed by 0.41), is then used as a basis for the SCODIS framework, which enables the discovery of in-demand skillsets based on the observation of skills clusters in a time series. The framework is used to conduct a time series forecasting experiment, resulting in the F-measures observed at 72%, which conﬁrms that to an extent, and with enough previous observations, it is indeed possible to identify which skillsets will dominate demand for a speciﬁc sector in the short-term.