Extracting contextual Information from scientific literature using CERMINE system

Abstract

CERMINEis a comprehensive open source system for extracting structured metadata and references from born-digital scientific literature. Among other information, the system is able to extract information related to the context the article was written in, such as the authors and their affiliations, the relations between them or references to other articles. Extracted information is presented in a structured, machine-readable form. CERMINE is based on a modular workflow, whose loosely coupled architecture allows for individual components evaluation and adjustment, enables effortless improvements and replacements of independent parts of the algorithm and facilitates future architecture expanding. The implementation of the workflow is based mostly on supervised and unsupervised machine-learning techniques, which simplifies the procedure of adapting the system to new document layouts and styles. In this paper we outline the overall workflow architecture, describe key aspects of the system implementation, provide details about training and adjusting of individual algorithms, and finally report how CERMINE was used for extracting contextual information from scientific articles in PDF format in the context of ESWC 2015 Semantic Publishing Challenge. CERMINE system is available under an open-source licence and can be accessed at http://cermine.ceon.pl.