Intelligent context based prediction using probabilistic intent-action ontology and tone matching algorithm

In many real life situations end results and basic starting data are known. To deduce conclusive evidence or to build holistic picture one needs to find out hidden information and missing text. This research paper delivers a novel algorithm (Probabilistic Intent-Action Ontology and Tone Matching Algorithm) to map multiple events on time line by determining their interdependency to predict the most probable series of events that might have occurred. Various aspects of flow of events, continuity, negation, shift of emotions etc. are considered. This algorithm is based on analyzing multidimensional intent and action relationships, application of naïve Bayes theorem to text, plotting relative hyperbolic probability and plotting the tone matching graph and calculating deviations. The proposed method shows very promising results. This approach can be used to solve many real life problems like solving criminal cases, completing stories, and identifying gaps in data.