Analysis of topics, theories, and methods of information systems research in the past two decades: A knowledge graph approach

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.

[1]  Stefan Hoyer,et al.  What are your Favorite Methods? - an Examination on the Frequency of Research Methods for is Conferences from 2006 to 2010 , 2012, ECIS.

[2]  Maria T. Pazienza,et al.  Information Extraction , 2002, Lecture Notes in Computer Science.

[3]  Heiko Paulheim,et al.  Knowledge graph refinement: A survey of approaches and evaluation methods , 2016, Semantic Web.

[4]  Evgeniy Gabrilovich,et al.  A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.

[5]  Andrew McCallum,et al.  Information extraction from research papers using conditional random fields , 2006, Inf. Process. Manag..

[6]  S. Waqar Jaffry,et al.  Information extraction from scientific articles: a survey , 2018, Scientometrics.

[7]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[8]  J. Leon Zhao,et al.  ISTopic: Understanding Information Systems Research through Topic Models , 2015, ICIS.

[9]  Anna Sidorova,et al.  Uncovering the Intellectual Core of the Information Systems Discipline , 2008, MIS Q..

[10]  Hsing Kenneth Cheng,et al.  Identifying Research Trends in IS , 2015, AMCIS.

[11]  Navonil Mustafee.  Evolution of IS research based on literature published in two leading IS journals - EJIS and MISQ , 2011, ECIS.

[12]  Frederick Reiss,et al.  An Algebraic Approach to Rule-Based Information Extraction , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[13]  Frank Puppe,et al.  UIMA Ruta: Rapid development of rule-based information extraction applications , 2014, Natural Language Engineering.

[14]  David Sontag,et al.  Learning a Health Knowledge Graph from Electronic Medical Records , 2017, Scientific Reports.

[15]  Chen Xu,et al.  A Rules and Statistical Learning Based Method for Chinese Patent Information Extraction , 2011, 2011 Eighth Web Information Systems and Applications Conference.

[16]  Xiaoming Zhang,et al.  MMKG: An approach to generate metallic materials knowledge graph based on DBpedia and Wikipedia , 2017, Comput. Phys. Commun..

[17]  Leyland F. Pitt,et al.  Potential Research Space in MIS: A Framework for Envisioning and Evaluating Research Replication, Extension, and Generation , 2002, Inf. Syst. Res..

[18]  Feng Xia,et al.  Two decades of information systems: a bibliometric review , 2018, Scientometrics.

[19]  Gabriele Beissel-Durrant.  A Typology of Research Methods Within the Social Sciences , 2004 .

[20]  Patrick Kenekayoro.  Identifying named entities in academic biographies with supervised learning , 2018, Scientometrics.

[21]  James P. Callan,et al.  Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding , 2017, WWW.

[22]  Mourad Gridach,et al.  Character-level neural network for biomedical named entity recognition , 2017, J. Biomed. Informatics.

[23]  Yogesh Kumar Dwivedi,et al.  A Methodology for Profiling Literature using Co-citation Analysis , 2010, AMCIS.

[24]  Ryan C. LaBrie.  Seeing 20/30: A Visual History of Key(word) Insights from MIS Quarterly , 2014, AMCIS.

[25]  Nigel P. Melville,et al.  Theories Used in Information Systems Research: Identifying Theory Networks in Leading IS Journals , 2009, ICIS.

[26]  Lars Mathiassen,et al.  Distilling a body of knowledge for information systems development , 2018, Inf. Syst. J..

[27]  Rudy Hirschheim,et al.  Reflections on Information Systems Journal's thematic composition , 2016, Inf. Syst. J..

[28]  Hsinchun Chen,et al.  Disease named entity recognition using semisupervised learning and conditional random fields , 2011, J. Assoc. Inf. Sci. Technol..