Cluster Analysis of Knowledge Development in the Field of Knowledge Extraction in Service Industry

Document Type : Research Paper

Authors

1 PhD Candidate, Knowledge and Information Science-Knowledge Management, Management and Economics Faculty, Tarbiat Modares University, Tehran, Iran.

2 Full-Prof., Knowledge and Information Science-Knowledge Management, Management and Economics faculty, Tarbiat Modares University, Tehran, Iran .

3 Assistant Prof., Knowledge and Information Science-Knowledge Management, Management and Economics faculty, Tarbiat Mo-dares University, Tehran, Iran.

4 Associate Professor, Department of Computer Science, Faculty of Mathematics, Tarbiat Modares University, Tehran, Iran

Abstract

Purpose: Service industries are recognized as one of the largest sectors of the economy globally, and it has the most prominent role in the countries' economic growth. To create an essential change that represents a revolutionary change in the technology of a product or service, there is a need to acquire, extract and develop knowledge to achieve a competitive advantage. Therefore, this study aims to analyze the knowledge development clusters in the service industry's knowledge extraction field. In the knowledge management process, knowledge extraction is the main phase of knowledge acquisition. Knowledge acquisition is one of the important aspects of knowledge discovery in databases to help managers make timely decisions by extracting correct knowledge.
Methodology: Bibliometrics and scientific mapping techniques have been used in this applied research. Research data were collected from the Scopus database from 1986 to 2022. VOSviewer and Bibliometrix R were used to analyze and visualize data and scientific maps. Furthermore, to ensure the accuracy and validity of the results, Bibliometrix and Excel tools have been used to integrate data and remove duplicate data.
Findings: The research findings show the knowledge extraction application among 434 documents in 5 clusters of knowledge extraction, artificial intelligence, information retrieval, semantics, and forecasting. In the research, knowledge extraction and data mining are the most widely used words in a single cluster and have the most centrality and betweenness. Likewise, the bibliometric analysis of the data in The Multiple Correspondence Analysis (MCA) shows that the Internet, natural language processing, and machine learning are among the topics that are important next to the healthcare sector. This shows the importance of natural language and machine learning in extracting knowledge in healthcare services. Since 2006, the importance of knowledge extraction has received more attention. The co-occurrence of keywords shows that knowledge extraction is widely used with data mining, extraction, and artificial intelligence. The keywords of knowledge extraction and data mining in cluster 1, semantics, knowledge management, and information services in cluster 2, and information retrieval, internet, and human in cluster 3 have the highest centrality. The theme mapping shows that forecasting, multi-agent systems, and planning are themes with high density and low centrality, which are called niche themes. Semantics, web services, and knowledge-based systems are the main themes with low density and high centrality. Also, artificial intelligence, information management, and decision support systems are themes with low density and centrality, which are also known as emerging or declining themes. The forecasting cluster is located in the strategic knowledge cluster group. Information retrieval, knowledge extraction, and artificial intelligence are included in the cluster of practical knowledge. Semantics as a cluster including various experts and specialists such as domain experts, knowledge engineers, and programmers is in the collaborative cluster.
Conclusion: Knowledge extraction is an emerging interdisciplinary field in knowledge management and has a direct and significant impact on the country's economy. Knowledge development and integration of key issues in knowledge extraction are essential. According to the findings of this study, for the promotion and advancement of this process in the service industry, it is suggested to provide a strategic view in the use of metadata analysis of the context of activity and success of the service industry in knowledge extraction. Moreover, knowledge management as the primary discipline and domain can guarantee success in this process. The clusters identified in this study are also divided into three practical, strategic, and collaborative knowledge clusters. Moreover, the results of this research can help managers of organizations, especially their knowledge managers, to plan and make decisions in the field of service industries to facilitate optimal knowledge extraction and maintain competitive advantage.

Keywords

References

 
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