The volume of scientific papers published annually in the biomedical domain is continuo us ly increasing. Streamlining the process of identifying the most critical and significant nuggets of information (such as hypotheses, observations, interventions, findings) in a given research publicatio n is a challenging but worthwhile task. This essential information, known as scientific artefacts, underpins the knowledge used by many health professionals in the decision-making process or researchers in creating systematic reviews; however most of today's search engines are unable to identify these artefacts.Evidence Based Medicine (EBM) represents a framework that encompasses decision-making in the healthcare domain, based on providing medical practitioners with the best available evidence so they can choose the optimum treatment for individual patients. In order to provide patients with the best treatment, health professionals need access to current, timely and reliable evidence retrieved from relevant published medical research or previously synthesised evidence. Hence, devising mechanisms that can automatically identify, retrieve, consolidate and present scientific artefacts, based on a given query, has the potential to greatly facilitate collating related evidence and ultimate ly streamline medical decision-making.This thesis represents an attempt to define a comprehensive framework for acquiring and managing scientific artefacts in the EBM domain -by transforming unstructured publications into structured, consolidated, pertinent knowledge. There have been previous attempts to model such information (e.g., supporting and contradicting statements), however these approaches have primarily focused on providing users with conceptual high-level frameworks and associated manual annotatio n services. The approach proposed in this thesis employs novel, sets of low-level features to unique ly identify key scientific information in EBM, and enable knowledge extraction and retrieval. This will also lead to automatic creation of networks of scientific artefacts, and eventually the detection of effects across diverse artefacts (i.e., new potential drug treatments). This goal will be attained by firstly modelling and extracting scientific artefacts from publications (more specifically, abstracts) and then consolidating and linking them using Linked Data approaches.The first step for pinpointing the best evidence in the published research is to formulate clinica l queries and their answers. Hence, a comprehensive and fine-grained model is essential to formula te key factors of evidence-based decision making according to various medical cases. The Problem/Population, Intervention, Comparison, and Outcome (PICO) framework is a specialised model to frame and answer a clinical or health care related question. An extension of PICO formalises this fundamental information by classifying it into six classes: Population, Intervention, Background, ii Outcome, Study Design, and Other (called the PIBOSO model). The PIBOSO model has be...