Explore, analyse and curate knowledge graphs in terms of traceability
“Glad you introduced a traceability process in your company. Now you are able to relate the various types of artifacts in order to build up a large traceability graph.” – But what are the next steps? How do you deal with the enormous amount of information? We provide you with use cases to explore, analyse and curate your traceability graphs. itemis ANALYZE is a professional traceability management system. It provides a full knowledge graph that may connect your whole development toolchain, including modelling tools and code. In addition to that we present possible applications of ML techniques to explore, analyse and curate your knowledge graph. We analyse the similarities of requirements in order to create a tailored ontology. This is the basis for your requirements-specific knowledge graph and further analysis. We will present the application of graph-based algorithms to analyse 8 aspects of your knowledge graph.
Sruthi Radhakrishnan is a graph enthusiast and IT consultant at itemis in Stuttgart. Her master thesis is based on creating a framework for missing link prediction on traceability graphs covering traditional to modern machine learning approaches. She completed her Master’s in information technology from the University of Stuttgart.
Ario Giancarlo Cecchettini is an IT Consultant at Itemis AG in Munich. He works on solutions in the areas of natural language processing, machine learning and cloud platforms. He studied Computational Linguistics (B.Sc.) at the Ludwig-Maximilians University in Munich.
Svenja Wendler works on model based development topics and project management at itemis in Lünen. In her thesis 2006 she analysed the applicability of association rules on large datasets to discover patterns in the internet traffic.