Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded

Machine Learning and Natural Language Processing Usage for Psychological Consultation

It`s an obvious fact to claim, that the problem of stress has become a vital issue presently. One of the main root cause of this is the escalation of human desires and complexity of those. Most of those desires would be difficult to achieve or not practical at all. When the reality is so harsh, compared with the imaginations, it will incur for stress. More the gap between reality and the perceptions, stress will escalate. As results of people are running after unrealistic or difficult to achieve greener pastures, most of them will end up with becoming a victim of stress. Stress management is a difficult skill to be developed, but in current context, it has become an essential skill to have. This research is based on the concept of internal self-talk. Thought stream captured in-form of text stream will be segmented, according to the cognitive behavioral therapeutic approach. This is technically implemented via POS tagging of the Stanford NLP library. Afterwards machine learning approach is used to train the WEKA engine, according to the OCEAN model, which is a prominent psychological model. Predictions derived from the trained WEKA model, will be presented inform of a report with the help of itext reporting plugin. This report will be used by the psychologist, before providing the treatment to the patient/client. It`s assumed, that this tool will be a good aiding tool, which can reduce the cognitive effort of a consultant. Respective, problem, technical, executional and all important aspects are addressed in detail, within this paper along with required evidences