Exploring semantic relationships and cross-disciplinary influences: case study of information systems and artificial intelligence

Abstract Generally, complex problems often require contributions from several research methods or research areas. The simultaneous use of several approaches gives occasion to the cross-fertilization between disciplines, which may even help the individual disciplines to develop. It is thus natural to ask how disciplines influence each other and if this can be studied using the best available methods. We approach this by computing semantic relationships within the publication records and new semantic measures are introduced to study the mutual influence. As an example, we examine the research areas of Artificial Intelligence (AI) and Information Systems (IS). It’s often stated that the study of IS is characterized using a wide range of research methodologies to examine and guide areas of interest within the field. This has led the IS community to perceive that it can guide AI by clarifying research questions and helping achieve goals. However, this doesn’t establish the core contribution of IS outside its domain. The similarity between the research areas is studied from multiple perspectives, such as conditional dependence, distance, citations/reference analysis, and journal analysis. The analysis and results are also studied with a perspective of bibliometric data from bibliographic indexing platforms such as Web of Science and Scopus. The semantic relationship and bibliometric analyses show that IS and AI concepts co-occur to some degree which reflects the interaction between the research areas. The role of AI has rocketed in recent years with top-cited AI papers having a greater impact on IS than IS papers on AI research.