WordNet: a lexical database for English
Communications of the ACM
A tutorial on spectral clustering
Statistics and Computing
Mining search engine query logs via suggestion sampling
Proceedings of the VLDB Endowment
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Users frequently pose comparison queries (e.g., ibm vs apple) on web search engines. However, little research has been done on understanding these queries. To fill in this gap, this paper describes a first solution to discovering and mining comparison queries. We present a novel snowballing algorithm that "crawls" comparison queries from search engines via their query autocompletion services. We propose a novel modeling approach that represents comparison queries in a comparison graph and develop a novel algorithm that mines closely related concepts from comparison graphs via spectral clustering. Initial experiments indicate that our approach can reveal the inherent semantic relationship among the concepts and discover different senses of a concept, e.g., "toyota" as a car brand or a company name.