  In recent years, with the rapid proliferation of research publications in the
field of Artificial Intelligence, it is becoming increasingly difficult for
researchers to effectively keep up with all the latest research in one's own
domains. However, history has shown that scientific breakthroughs often come
from collaborations of researchers from different domains. Traditional search
algorithms like Lexical search, which look for literal matches or synonyms and
variants of the query words, are not effective for discovering cross-domain
research papers and meeting the needs of researchers in this age of information
overflow. In this paper, we developed and tested an innovative semantic search
engine, Analogy Search Engine (ASE), for 2000 AI research paper abstracts
across domains like Language Technologies, Robotics, Machine Learning,
Computational Biology, Human Computer Interactions, etc. ASE combines recent
theories and methods from Computational Analogy and Natural Language Processing
to go beyond keyword-based lexical search and discover the deeper analogical
relationships among research paper abstracts. We experimentally show that ASE
is capable of finding more interesting and useful research papers than baseline
elasticsearch. Furthermore, we believe that the methods used in ASE go beyond
academic paper and will benefit many other document search tasks.
