Mining Domain Similarity to Enhance Digital Indexing

Indexing research articles in scientific publications can be arduous. The authors tag their articles by the topics or domains relevant to their research. A publication's organizers may tag them by the broad topics of the specific publication. A third-party may index or tag these articles based on their subject knowledge. Hence indexing of articles can be uneven due to inconsistencies in area knowledge by third-parties or the niche topic representation by the authors. Publications may have schemes in place for indexing or tagging the articles but such schemes cannot keep up with the continuously changing landscape of research. These schemes may need to be updated with newer topics or domains being churned out by the state of the art research. Our technique endeavors to address this problem. We present a methodology to find similarity among domains extracted from the content of research papers, and cluster related domains. Analysis of these clusters provides insights into how the existing indexing schemes may be enhanced by adding newer domains.