Understanding the development of research fields is an important task for researchers. Previous studies on analyzing Information Systems (IS) research mainly focus on document level analysis or latent topic analysis. Great expert efforts are required in order to gain useful insights from the analysis. With the increasingly large number of academic publications in the IS field, it is critical to utilize advanced techniques to extract finer knowledge automatically for a better understanding of the field. In this research, we use machine learning methods to automatically construct an IS knowledge graph. The knowledge graph contains research topics, theories, methods, and their relationships extracted from scientific papers published between 1999 and 2018 in eight IS leading journals. We then employ it to analyze IS research at a fine level. A series of examples demonstrate the effectiveness of the knowledge graph approach. This study is the first attempt that uses knowledge graph to analyze IS research and it helps researchers better understand the development of IS field without much human labor.
Deng, Weiwei; Huang, Xiaoming; Yuan, Hui; Ma, Jian; and Wang, Gang, "Analysis of topics, theories, and methods of information systems research in the past two decades: A knowledge graph approach" (2020). PACIS 2020 Proceedings. 3.
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