13. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising: identifying, a business rule from a text document, wherein the business rule comprises one or more rule intents, wherein one or more rule intent comprise at least one of keywords, parts of speech (POS) tags, and wildcards and said one or more rule intents are utilized to automatically identify the business rule from the text document; creating, via the one or more hardware processors, a rule repository based on an identified business rule and the one or more rule intents, wherein the rule intents are atomic constraints stored in an ontology form; comparing, the one or more rule intents in the business rule with the one or more clusters associated with a plurality of rule types in the rule repository to compute a match score, wherein a match score is indicative of number of rule intents and based on the match score, the business rule is annotated with one or more rule types in the rule repository, and wherein the rule repository is periodically updated with a plurality of new rule intents, said rule intent patterns and new rule types; classifying, the business rule under at least one rule type, amongst the plurality of rule types, to further classify the business rule into at least one of formatted and unformatted text documents for referring and tracing the text document by annotating the business rule with one or more corresponding rule intents and one or more rule types in the rule repository; clustering, said plurality of new rule intents in an agglomerative manner to identify one or more clustering rule intents that co-occur frequently in the rule repository; assembling, the one or more clusters into a plurality of clustering techniques, wherein the rule repository is periodically updated with the corresponding rule intent and the rule type to classify sentences obtained from a training dataset; and automatically associating, via the one or more hardware processors, the business rules extracted from the text document with a plurality of corresponding knowledge element types and system components to reference, trace and re-use at least one of requirement artifacts and system documentation.
13. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method comprising: identifying, a business rule from a text document, wherein the business rule comprises one or more rule intents, wherein one or more rule intent comprise at least one of keywords, parts of speech (POS) tags, and wildcards and said one or more rule intents are utilized to automatically identify the business rule from the text document; creating, via the one or more hardware processors, a rule repository based on an identified business rule and the one or more rule intents, wherein the rule intents are atomic constraints stored in an ontology form; comparing, the one or more rule intents in the business rule with the one or more clusters associated with a plurality of rule types in the rule repository to compute a match score, wherein a match score is indicative of number of rule intents and based on the match score, the business rule is annotated with one or more rule types in the rule repository, and wherein the rule repository is periodically updated with a plurality of new rule intents, said rule intent patterns and new rule types; classifying, the business rule under at least one rule type, amongst the plurality of rule types, to further classify the business rule into at least one of formatted and unformatted text documents for referring and tracing the text document by annotating the business rule with one or more corresponding rule intents and one or more rule types in the rule repository; clustering, said plurality of new rule intents in an agglomerative manner to identify one or more clustering rule intents that co-occur frequently in the rule repository; assembling, the one or more clusters into a plurality of clustering techniques, wherein the rule repository is periodically updated with the corresponding rule intent and the rule type to classify sentences obtained from a training dataset; and automatically associating, via the one or more hardware processors, the business rules extracted from the text document with a plurality of corresponding knowledge element types and system components to reference, trace and re-use at least one of requirement artifacts and system documentation. 15. The non-transitory computer-readable medium as claimed in claim 13 , wherein the classifying comprises annotating the one or more rule intents in the business rule with the at least one rule type.