Establishing interoperability among knowledge organization systems for research management: a social network approach

Abstract

Knowledge organization systems (KOS) have long been established as a tool to represent organized interpretation of knowledge structures. Various types of KOS such as discipline tree for research projects, subject categories for research publications, and classifications schemes for research patents have been constructed and widely used in R&D contexts. However, the incompatible KOS, together with information proliferation in the Big Data Era, impose great challenges for effective research management. How to facilitate interoperability among heterogeneous research information sources is an important problem to be solved. KOS mapping methods were proposed to provide alternative subject access by establishing equivalence or partial equivalence relationships between classes in different KOS but suffered from “lagging mapping” and “deficient mapping”. This research proposes a social network approach that leverages online social platform information (i.e., research activities and social activities) for KOS mapping. The underlying assumption behind the approach is that “two classes/terms in different KOS are related if their corresponding research objects are connected to similar researchers”. The social network approach leverages social network analysis instead of semantic and structure analysis of metadata information for mapping degree calculation. The approach has been implemented on the largest research social platform in China and successfully realizes mapping between KOS for research management purpose. We conducted mapping between National Natural Science Foundation of China discipline tree and Web of Science subject categories in this study to examine the performance of social network approach.