AlgorithmSeer: A System for Extracting and Searching for Algorithms in Scholarly Big Data
Suppawong Tuarob, Sumit Bhatia, Prasenjit Mitra, Clyde Lee Giles · IEEE Transactions on Big Data · 2016
Algorithms are usually published in scholarly articles, especially in the computational sciences and related disciplines. The ability to automatically find and extract these algorithms in this increasingly vast collection of scholarly digital documents would enable algorithm indexing, searching, discovery, and analysis. Recently, AlgorithmSeer, a search engine for algorithms, has been investigated as part of CiteSeer' with the intent of providing a large algorithm database. Currently, over 200,000 algorithms have been extracted from over 2 million scholarly documents. This paper proposes a novel set of scalable techniques used by AlgorithmSeer to identify and extract algorithm representations in a heterogeneous pool of scholarly documents. Specifically, hybrid machine learning approaches are proposed to discover algorithm representations. Then, techniques to extract textual metadata for each algorithm are discussed. Finally, a demonstration version of AlgorithmSeer that is built on Solr/Lucene open source indexing and search system is presented.