10. A computer-implemented method of extracting semantic bio-entity relationships or patterns front non-annotated data by natural language processing and graph theoretic algorithm, comprising the steps of: receiving a plurality of known bio-entity strings and a plurality of interaction word strings; receiving annotated text as training data that contains true and false patterns; automatically building a decision support tool based on said true and false patterns to which said non-annotated data can be parsed, said decision support tool including at least a first level and a second level, said first level having a first decision node, said second level having a second decision node, said first and second decision nodes each associated with at least a portion of said true and false patterns; receiving said non-annotated data, extracting a textual clause of said non-annotated data that contains non-triplet word strings and at least one triplet, said at least one triplet including a first bio-entity, a second bio-entity, and an interaction word, wherein said interaction word indicates a possible relationship between said first bio-entity and said second bio-entity; automatically parsing said extracted textual clause through said decision support tool to obtain a plurality of components based on dependencies among said plurality of components; extracting said at least one triplet from said plurality of components by attempting to match said plurality of components of said parsed, extracted textual clause to said first level of said decision support tool; identifying extraction of said at least one triplet as true if said plurality of components matches said first level of said decision support tool; identifying extraction of said at least one triplet as false if said plurality of components fails to match said first level of said decision support tool; as a result of said plurality of components failing to match said first level of said decision support tool, extracting said at least one triplet from said plurality of components by attempting to match said plurality of components to said second level of said decision support tool; identifying extraction of said at least one triplet as true if said plurality of components matches said second level of said decision support tool, said second level of said decision support tool being a simplified pattern of said first level of said decision support tool to capture textual clauses that are not identical to said extracted textual clause; and identifying extraction of said at least one triplet as false if said plurality of components fails to match said second level of said decision support tool.
10. A computer-implemented method of extracting semantic bio-entity relationships or patterns front non-annotated data by natural language processing and graph theoretic algorithm, comprising the steps of: receiving a plurality of known bio-entity strings and a plurality of interaction word strings; receiving annotated text as training data that contains true and false patterns; automatically building a decision support tool based on said true and false patterns to which said non-annotated data can be parsed, said decision support tool including at least a first level and a second level, said first level having a first decision node, said second level having a second decision node, said first and second decision nodes each associated with at least a portion of said true and false patterns; receiving said non-annotated data, extracting a textual clause of said non-annotated data that contains non-triplet word strings and at least one triplet, said at least one triplet including a first bio-entity, a second bio-entity, and an interaction word, wherein said interaction word indicates a possible relationship between said first bio-entity and said second bio-entity; automatically parsing said extracted textual clause through said decision support tool to obtain a plurality of components based on dependencies among said plurality of components; extracting said at least one triplet from said plurality of components by attempting to match said plurality of components of said parsed, extracted textual clause to said first level of said decision support tool; identifying extraction of said at least one triplet as true if said plurality of components matches said first level of said decision support tool; identifying extraction of said at least one triplet as false if said plurality of components fails to match said first level of said decision support tool; as a result of said plurality of components failing to match said first level of said decision support tool, extracting said at least one triplet from said plurality of components by attempting to match said plurality of components to said second level of said decision support tool; identifying extraction of said at least one triplet as true if said plurality of components matches said second level of said decision support tool, said second level of said decision support tool being a simplified pattern of said first level of said decision support tool to capture textual clauses that are not identical to said extracted textual clause; and identifying extraction of said at least one triplet as false if said plurality of components fails to match said second level of said decision support tool. 11. A computer-implemented method as in claim 10 , further comprising instructions for: establishing a threshold probability value based on probable accuracy retrieved from said decision support tool; said step of extracting said at least one triplet from said extracted textual clause by attempting to match said extracted textual clause to said first level, including: tagging said extracted textual clause with a first probability value based on said accurate interaction between said extracted first and second bio-entities and said extracted interaction word within said first level; classifying said at least one triplet as true as a result of said first probability value meeting said threshold probability value, and classifying said at least one triplet as false as a result of said first probability value failing to meet said threshold probability value; said step of extracting said at least one triplet from said extracted textual clause by attempting to match said extracted textual clause to said second level, including: tagging said extracted textual clause with a second probability value based on said accurate interaction between said extracted first and second bio-entities and said extracted interaction word within said second level; classifying said at least one triplet as true as a result of said second probability value meeting said threshold probability value, and classifying said at least one triplet as false as a result of said second probability value failing to meet said threshold probability value.