A Novel Improvement to Google Scholar Ranking Algorithms Through Broad Topic Search

Kearl, M., Noteboom, C., Tech, D. (2017
A Novel Improvement to Google Scholar Ranking Algorithms Through Broad Topic Search.  
Proceedings of the Twelfth Midwest Association for Information Systems Conference, Springfield, Illinois May 18-19, 2017

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match and citation count to sort its results. This paper presents a novel improvement to Google Scholar algorithms by aggregating multiple synonymous searches into one set of results, offsetting the necessity to guess all potential search phrases for a research topic. This design science research method uses a broad topic analysis that examines search queries, finds synonymous phrases, and combines all keyword searches into one set of results based on current Google Scholar citation count algorithms. To support and evaluate this research-in-progress, several users will compare multiple niche search queries against old and new algorithms. The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research.