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https://www.selleckchem.com/products/heptadecanoic-acid.html When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see. We have previously created a study cohort ontology to standardize this information and make it accessible for knowledge-based decision support. The extraction of this information from research publications is challenging, however, given the wide variance in reporting cohort characteristics in a tabular representation. To address this issue, we have developed an ontology-enabled knowledge extraction pipeline for automatically constructing knowledge graphs from the cohort characteristics found in PDF-formatted research papers. We evaluated our approach using a training and test set of 41 research publications and found an overall accuracy of 83.3% in correctly assembling the knowledge graphs. Our research provides a promising approach for extracting knowledge more broadly from tabular information in research publications.New medical research concerning the spine and its diseases are incrementally made available through biomedical literature repositories. Several Natural Language Processing (NLP) tasks, like Semantic Role Labelling (SRL) and Information Extraction (IE), can offer support for, automatically, extracting relevant information about spine, from scientific papers. This paper presents a domain-specific FrameNet, called SpiNet, for automatic information extraction about spine concepts and their semantic types. For this, we use the frame semantic and the MeSH ontology in order to extract the relevant information about a disease, a treatment, a medication, a sign or symptom, related to spine medical domain. The differential of this work is the enrichment of SpiNet's base with the MeSH ontology, whose terms, concepts, descriptors and semantic types enable automatic se

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