Smart data integration by goal driven ontology learning

Abstract

The smart data integration approach is proposed to compose data and knowledge of the different nature, origin, formats and standards. This approach is based on the selective goal driven ontology learning. The automated planning paradigm in a combination with a value of the perfect information approach is proposed to be used for evaluating the knowledge correspondence with the learning goal for the data integration domain. The information model of a document is represented as a supplement to the Partially Observable Markov Decision Process (POMDP) strategy of a domain. It helps to estimate the document a pertinence as the increment of the strategy expected utility. A statistical method for identifying the semantic relations in the natural language texts for their linguistic characteristics is developed. It helps to extract the Ontology Web Language (OWL) predicates from the natural language text using data about sub semantic links. A set of methods and means based on ontology learning was developed to support the smart data integration process. A technology uses the Natural Language Processing software Link Grammar Parser, WordNet Application Programming Interface (API) as well as the OWL API.