14. A semantic tagging method that outputs semantically linked tags for text content comprising: inputting the text content to a computer; by the computer, extracting nouns and noun phrases from the text content; by the computer, mapping the extracted nouns and noun phrases to terms of an ontology; by the computer, mapping the extracted nouns and noun phrases to a correct sense of the ontology terms using lexical chaining Word Sense Disambiguation (WSD) algorithms; by the computer, weighting the extracted nouns' and noun phrases' concepts from their ontological and statistical features; by the computer, extracting key-phrases from the weighted concepts; and by the computer, outputting the key-phrases as semantic tags; wherein mapping the extracted nouns and noun phrases to a correct sense of the ontology terms further comprises: creating a hypernym lexical chain with WSD scores for senses of the input nouns and noun phrases; creating a domain lexical chain with WSD scores for senses of the input nouns and noun phrases; creating a holonym lexical chain with WSD scores for senses of the input nouns and noun phrases; creating a category lexical chain with WSD scores for senses of the input nouns and noun phrases; normalizing the WSD scores in each of the hypernym, domain, category and holonym lexical chains; generating a WSD score using a sense rank algorithm; assigning weights to each WSD score and summing the weighted WSD scores for the hypernym, domain, category, holonym lexical chains, and the sense rank algorithm; evaluating the hypernym, domain, category and holonym lexical chains, the sense rank algorithm and optimizing their weights for a given target data; and mapping the nouns and noun phrases to a highest ranked sense.
14. A semantic tagging method that outputs semantically linked tags for text content comprising: inputting the text content to a computer; by the computer, extracting nouns and noun phrases from the text content; by the computer, mapping the extracted nouns and noun phrases to terms of an ontology; by the computer, mapping the extracted nouns and noun phrases to a correct sense of the ontology terms using lexical chaining Word Sense Disambiguation (WSD) algorithms; by the computer, weighting the extracted nouns' and noun phrases' concepts from their ontological and statistical features; by the computer, extracting key-phrases from the weighted concepts; and by the computer, outputting the key-phrases as semantic tags; wherein mapping the extracted nouns and noun phrases to a correct sense of the ontology terms further comprises: creating a hypernym lexical chain with WSD scores for senses of the input nouns and noun phrases; creating a domain lexical chain with WSD scores for senses of the input nouns and noun phrases; creating a holonym lexical chain with WSD scores for senses of the input nouns and noun phrases; creating a category lexical chain with WSD scores for senses of the input nouns and noun phrases; normalizing the WSD scores in each of the hypernym, domain, category and holonym lexical chains; generating a WSD score using a sense rank algorithm; assigning weights to each WSD score and summing the weighted WSD scores for the hypernym, domain, category, holonym lexical chains, and the sense rank algorithm; evaluating the hypernym, domain, category and holonym lexical chains, the sense rank algorithm and optimizing their weights for a given target data; and mapping the nouns and noun phrases to a highest ranked sense. 21. The method according to claim 14 wherein creating a category lexical chain further comprise: connecting senses defined in a same category via a category concept node to create a category lexical chain; scoring a sense according to the number of concepts within the category lexical chain; and mapping a noun and noun phrase to a sense that receives the highest WSD score among all senses of the noun and noun phrase.