Contact Person: Levii Smith
Contact Email: email@example.com
The abundance of academic publications coupled with the highly specific nature of each publication makes the process of research extremely tedious. Existing academic search engines generally rank results by relevance to query keywords. Furthermore, the results are returned as a long list of links, which is easy to skim but difficult to actually use. These characteristics (based on web search) are not ideal for the domain of academic publications – when doing research, we want to know what the important papers are as well as the relevant ones, and also easily navigate between papers while remaining aware of the context of the search.
One useful measure of importance is number of citations (used by e.g. MS Academic Search and Google Scholar). We propose using this reference information to visualize the local neighborhood of a paper within the larger graph of academic publications. This is similar to the MAS citation graph, but would present both forward and backward references as well as make the vital features of importance and relevance more obvious to the user. This would facilitate rapid discovery of important information in fields unfamiliar to the user.