Algo_Seer: System For Extracting and Searching Algorithms In Scholarly Big Data

Abstract

Algorithms are the crucial and important part for any research and developments. Algorithms are usually published in the scientific publications, journals, conference papers or thesis. Algorithms plays important role especially in the computational and research areas where the researchers and developers look for the innovations. Therefore there is need for a search system which automatically searches for algorithms from the scholarly big data. Algo_Seer is been proposed as part of CiteSeer system which automatically searches for pseudo codes and algorithmic procedures and performs indexing, analysis and ranking to extract the algorithms. This work proposes a search system Algo_Seer which utilizes a novel arrangement of procedures such as rule based method, machine learning methods to recognize, separate and extract the calculated algorithms from the academic reports. Particularly mixture troupe machine learning systems are utilized to obtain the efficient results. © 2020, Springer Nature Switzerland AG.