Ontology network analysis for safety learning in the railway domain

Ontologies have been used in diverse areas such as Knowledge Management (KM), Artificial Intelligence (AI), Natural Language Processing (NLP) and Semantic Web as they allow software applications to integrate, query and reason about concepts and relations within a knowledge domain. For Big Data Risk Analysis (BDRA) in railways, ontologies are a key enabler for obtaining valuable insights into safety from the large amount of data available from the railway. Traditionally, the ontology building has been an entirely manual process that has required a considerable human effort and development time. During the last decade, the in-formation explosion due to the Internet and the need to develop large-scale methods to extract patterns in a systematic way, has given rise the research area of “ontology learning”. Despite recent research efforts, ontol-ogy learning systems are still struggling with extracting terms (words or multiple-words) from text-based data. This manuscript explores the benefits of visual analytics to support the construction of ontologies for a particular part of railway safety management: possessions. In railways, possession operations are the protection arrangements for engineering work that ensure track workers remain separated from moving trains. A network of terms from possession operations standards is represented to extract the concepts of the ontology that enable the safety learning from events related to possession operations.