Abstract: Protecting consumer data is a key responsibility of an organization. The method of protection need to compare the data with policy documents. Ontology and a threshold to select an optimal match plays a key role in such comparison. The conventional automatic threshold computation methods are complex and not based on semantic similarity. The present disclosure generates an Entity Relationship (ER) model from an input document and is converted into a first ontology. The first ontology and a second ontology obtained from a relational database are compared by an ontology matching algorithm. Further, the plurality of many to many correspondences are optimized to one to one correspondence by an optimization method. Further, a plurality of optimal one to one correspondence is generated based on a threshold. The threshold is computed based on symmetric and transitive property. Further, semantically similar entities are selected based on the optimal one to one correspondence.

Abstract: Protecting consumer data is a key responsibility of an organization. The method of protection need to compare the data with policy documents. Ontology and a threshold to select an optimal match plays a key role in such comparison. The conventional automatic threshold computation methods are complex and not based on semantic similarity. The present disclosure generates an Entity Relationship (ER) model from an input document and is converted into a first ontology. The first ontology and a second ontology obtained from a relational database are compared by an ontology matching algorithm. Further, the plurality of many to many correspondences are optimized to one to one correspondence by an optimization method. Further, a plurality of optimal one to one correspondence is generated based on a threshold. The threshold is computed based on symmetric and transitive property. Further, semantically similar entities are selected based on the optimal one to one correspondence.

Abstract: A crowdsource assistant provides guidance to crowdsource resources navigating a crowdsource platform to bid for and be accepted to accomplish tasks. The crowdsource assistant utilizes machine learning to train similarity computation models that improve the crowdsource assistant's computing capability to generate more relevant recommendations to a resource in a faster and more efficient manner.

Abstract: A crowdsource assistant provides guidance to crowdsource resources navigating a crowdsource platform to bid for and be accepted to accomplish tasks. The crowdsource assistant utilizes machine learning to train similarity computation models that improve the crowdsource assistant's computing capability to generate more relevant recommendations to a resource in a faster and more efficient manner.