Constituent vs Dependency Parsing-Based RDF Model Generation from Dengue Patients' Case Sheets

Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document's contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients' documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models' exhaustiveness in terms of the number of facts that are transformed into RDF representations.