System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for generating an entity relationship model, the method comprising a processor implemented steps of: receiving, by the processor, a plurality of documents as a first input; receiving, by the processor, an existing entity relationship model as a second input; training an information extraction model using the first input and the second input using a training module; validating the trained information extraction model; providing a data source as input to a knowledge generation module; extracting one or more features from the data source using the trained information extraction model; setting a pattern based on one or more extracted features; and generating the entity relationship model corresponding to the data source based on the set pattern using the knowledge generation module. 2. The method of claim 1, wherein at least one of the plurality of documents are unannotated documents or partially annotated documents. 3. The method of claim 1, wherein the entities can be extracted using at least one of a noun verb collocation approach and a dependency based parsing. 4. The method of claim 3, wherein the noun verb collocation approach comprises the steps of: identifying all the possible entities in the plurality of documents; identifying all the entity relationships in the plurality of documents; and filtering to remove the unwanted entities and the unwanted entity relationships, wherein the filtering results in generation of positive and negative entities. 5. The method of claim 3, wherein the dependency based parsing comprises the steps of: identifying a list of action verbs; extracting a list of nouns that are in the dependency relation with the list of action verbs; extracting tuples with two nouns and an action verb connected through two dependency relations; and relating at least two entities of the extracted tuples with a connecting action. 6. The method of claim 1, wherein the training module configured to perform the steps of: cleaning unnecessary data from the first input and the second input; converting the cleaned data into a structured format, wherein the structured format is annotated data; extracting one or more features from the annotated data; generating patterns based on user annotated documents; and setting a pattern using the generated patterns. 7. The method of claim 1, wherein the one or more feature comprises one or more entities, one or more verbs, one or more attributes, one or more conditions or relationship between the entities and the attributes. 8. The method of claim 1 wherein the information extraction model extracts multi-arity relations, conjunctive relations or negative relations. 9. The method of claim 1 wherein the information extraction model is configured to perform stemming, co-reference resolution, chunk parsing, part of speech tagging and dependency parsing. 10. The method of claim 1, wherein the data source comprises at least one of documents, a database, a web site, a web page, a blog, a web corpus like stack-overflow, images, pictures or sounds. 11. The method of claim 7, wherein the documents comprise user manual, technical manuals or standard operational procedures. 12. The method of claim 1 further includes the step of storing the generated entity relationship model in an entity relationship model repository and updating the ontology in the entity relationship model repository. 13. The method of claim 12, wherein the knowledge stored in the entity relationship model repository may be used as a feedback for generating a new entity relationship model. 14. The method of claim 1 further include the step of transferring the learning of the ER model from one database to another database. 15. The method of claim 1 further includes the step of providing multi-level validation for entity relationship model generation. 16. A system for generating an entity relationship model, the system comprises: a user interface for providing a plurality of documents as a first input and an existing entity relationship model as a second input; a memory; and a processor in communication with the memory, wherein the processor comprises: a training module to train an information extraction model using the first input and the second input; a validation module for validating the trained information extraction model; an extraction module for extracting one or more features from the data source using the trained information extraction model; and a knowledge generation module for generating the entity relationship model using a set pattern based on one or more extracted features. 17. The system of claim 16, further includes an entity relationship model repository for storing the generated entity relationship model. 18. A non-transitory computer-readable medium having embodied thereon a computer program for generating an entity relationship model, the method comprising: receiving, by the processor, a plurality of documents as a first input; receiving, by the processor, an existing entity relationship model as a second input; training an information extraction model using the first input and the second input using a training module; validating the trained information extraction model; providing a data source as input to a knowledge generation module; extracting one or more features from the data source using the trained information extraction model; setting a pattern based on one or more extracted features; and generating the entity relationship model corresponding to the data source based on the set pattern using the knowledge generation module.