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In recent years, with the rapid proliferation of research publications in thefield of Artificial Intelligence, it is becoming increasingly difficult forresearchers to effectively keep up with all the latest research in one's owndomains. However, history has shown that scientific breakthroughs often comefrom collaborations of researchers from different domains. Traditional searchalgorithms like Lexical search, which look for literal matches or synonyms andvariants of the query words, are not effective for discovering cross-domainresearch papers and meeting the needs of researchers in this age of informationoverflow. In this paper, we developed and tested an innovative semantic searchengine, Analogy Search Engine (ASE), for 2000 AI research paper abstractsacross domains like Language Technologies, Robotics, Machine Learning,Computational Biology, Human Computer Interactions, etc. ASE combines recenttheories and methods from Computational Analogy and Natural Language Processingto go beyond keyword-based lexical search and discover the deeper analogicalrelationships among research paper abstracts. We experimentally show that ASEis capable of finding more interesting and useful research papers than baselineelasticsearch. Furthermore, we believe that the methods used in ASE go beyondacademic paper and will benefit many other document search tasks.

Analogy Search Engine: Finding Analogies in Cross-Domain Research Papers