Semantics in Social Tagging Systems: A Systematic Review

The objective of the present study has been to systematically review semantic research studies on social tagging systems in order to identify the researchers’ areas of interest, to investigate the impact of semantic issues on information retrieval in ​such systems, and to identify research gaps in this area​​.

Ninety-eight studies were found by searching ​ relevant ​databases. After initial investigation and consultation with two specialists in the field, 41 studies published in 2003-2018 were reviewed.

Important topics of semantic research on social tagging systems include producing an automatic semantic tagging algorithm, designing a semantic tagging system, producing an algorithm, extracting hierarchical relationship from a set of tags, and using WordNet to determine semantic relationships among tags. In addition, research gaps identified include devising a method for identifying sources containing a specific meaning of a tag without having to review all sources, exploring the possibility of using clustering methods to cluster sources or users of folksonomies, and designing a semantic tagging system which is user-friendly. All of these issues should be taken into account in future research.

Given the gaps identified, the subject of semantics in tagging systems needs further investigation, as it has a direct impact on search and retrieval by these systems.