A novel improvement to google scholar algorithms through broad topic search.

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

A Proposed Improvement to Google Scholar Algorithms Through Broad Topic Search

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match that excludes synonymous search terms that may be overlooked or neglected by researchers. This paper aims to improve on the current Google Scholar Search System by allowing a broad topic search algorithm to diversify and allow synonymous search terms to be included and ranked with other results. The authors propose a Design Science method to improve the Google Scholar Search System by developing a broad topic prototype that will add synonymous keywords into Google Scholar ranking algorithms. The results from twenty users will be evaluated by means of Mean Reciprocal Rank and Discounted Cumulative Gain. This improvement will introduce a modern approach to academic search engines systems, and to allow researchers who overlook potential search queries, an improved core topic diversity, quality, and discoverability of published research

A Proposed Improvement to Google Scholar Algorithms Through Broad Topic Search Emergent Research Forum Paper

Google Scholar uses ranking algorithms to find the most relevant academic research possible. However, its algorithms use an exact keyword match that excludes synonymous search terms that may be overlooked or neglected by researchers. This paper aims to improve on the current Google Scholar Search System by allowing a broad topic search algorithm to diversify and allow synonymous search terms to be included and ranked with other results. The authors propose a Design Science method to improve the Google Scholar Search System by developing a broad topic prototype that will add synonymous keywords into Google Scholar ranking algorithms. The results from twenty users will be evaluated by means of Mean Reciprocal Rank and Discounted Cumulative Gain. This improvement will introduce a modern approach to academic search engines systems, and to allow researchers who overlook potential search queries, an improved core topic diversity, quality, and discoverability of published research