What Were People Searching For? A Query Log Analysis of An Academic Search Engine

Abstract

Academic search engines have served the research community for years, yet there is little work done on understanding the taxonomy of query semantics. In this work, we present our findings of analyzing the query log of an academic search engine in the past four years. We study the distribution of query intents to understand the information requested by users. We classify query strings by topics using shallow and latent features captured using a customized word embedding model. To this end, we create a dataset that has scientific keywords and titles labeled with fields of study. This dataset is later used to train a classifier that discriminates query logs by topics. Our work will help to train better learning-based ranking functions that improve user experiences for an academic search engine. In addition, we anonymize our 14,759,852 query logs and make them available to the research community for further exploration.