An Automatic Workflow for Formalization of Scholarly Articles' Structural and Semantic Elements

We present a workflow for the automatic transformation of scholarly literature to a Linked Open Data (LOD) compliant knowledge base to address Task 2 of the Semantic Publishing Challenge 2016. In this year's task, we aim to extract various contextual information from full-text papers using a text mining pipeline that integrates LOD-based Named Entity Recognition (NER) and triplification of the detected entities. In our proposed approach, we leverage an existing NER tool to ground named entities, such as geographical locations, to their LOD resources. Combined with a rule-based approach, we demonstrate how we can extract both the structural (e.g., floats and sections) and semantic elements (e.g., authors and their respective affiliations) of the provided dataset’s documents. Finally, we integrate the LODeXporter, our flexible exporting module to represent the results as semantic triples in RDF format. As the result, we generate a scalable, TDB-based knowledge base that is interlinked with the LOD cloud, and a public SPARQL endpoint for the task’s queries. Our submission won the second place at the SemPub2016 challenge Task 2 with an average 0.63 F-score.