Abstract: Embodiments of the present invention relate to a system for data integration and information retrieval by bringing semantically related data together for a given context. As described, the integration of data may include the building of an ontology, the mapping of one or more processes, semantic maps and concept dictionaries in the ontology to one or more data sources, tagging the data sources in accordance with the ontology, providing a query interface for accepting an input query from a user, the mapping of the input query to one or more concepts in the ontology, and deriving one or more subqueries thereby, and the querying of data sources in accordance with the composed one or more subqueries, wherein the data sources queried are tagged with one or more concepts from the ontology. Additionally, the tracking of data across data sources in accordance with a defined data value chain is disclosed.

Abstract: The various embodiments herein provide a method and system for building a relationship hierarchy from big data. The method comprises extracting a plurality of relationships defined between entities from a big data, building relationship recognition models adapted to identify different forms of generic relationships, resolving the relationships by grouping the similar relationships together and separating the relationships which are syntactically and semantically dissimilar and reconciling the resolved relationships to build the relationship hierarchy. The relationship hierarchy comprises groups and subgroups of relationships created based on generic relationship similarity based on a contextual aspect and a specialization aspect using a Language and Domain model. The method of extraction of plurality of relationships from the unstructured data is a self-learning process which uses open information extraction techniques for learning new relationships.

Abstract: The various embodiments herein provide a system and method for mining frequent patterns in relationship space from a plurality of relationship sequences extracted from a big data The system comprises a data repository for collecting and storing the big data. An Entity Store for collecting and storing a plurality of entities from the big data, an Entity Hierarchy for representing a hierarchical structure of entities, a Relationship Store for collecting and storing relationship instances between the pluralities of entities, a Relationship Hierarchy for representing a hierarchical structure of relationship, a language/domain model for organizing entities and relationships in a hierarchical manner, a pattern query Processing Module (PQPM) for processing, a pattern query related to finding patterns in relationships and entities, and a Pattern Generation Module (PGM) to generate frequent patterns and a Frequent Pattern Display Module (FPDM) to provide a visual presentation of the mined patterns.

Abstract: The various embodiments herein provide a method and system for building a relationship hierarchy from big data. The method comprises extracting a plurality of relationships defined between entities from a big data, building relationship recognition models adapted to identify different forms of generic relationships, resolving the relationships by grouping the similar relationships together and separating the relationships which are syntactically and semantically dissimilar and reconciling the resolved relationships to build the relationship hierarchy. The relationship hierarchy comprises groups and subgroups of relationships created based on generic relationship similarity based on a contextual aspect and a specialization aspect using a Language and Domain model. The method of extraction of plurality of relationships from the unstructured data is a self-learning process which uses open information extraction techniques for learning new relationships.

Abstract: The various embodiments herein provide a method and a system for building an entity hierarchy. The method comprises extracting a plurality of entities from a bin data, determining a parent entity by understanding a context in which the entity is used, resolving the entities by bringing the synonymous entities together and holding the polysemous entities apart based on a semantic context and a syntactic context and building a hierarchical structure of entities using knowledge repositories, ontologies and language repositories along with natural language processing techniques. The method of extracting entities from the structured data comprises identifying each data point as an entity and identifying entities based on a relationship defined with other entities. The method of extracting entities from unstructured data includes a self-learning process and training based learning process to learn new parent entities from domain specific documents using new entity recognition models.

Abstract: The various embodiments herein describe a method for providing information visualization comprising identifying a plurality of events from a big data, calculating a temporal distance between at least two events, calculating a semantic distance between the at least two events, storing the calculated semantic distance and the temporal distance with respect to the at least two events in a data structure, providing the semantic distance and the temporal distances calculated between the at least two events on a visual representation, identifying one or more relevant events with respect to the target event based on the semantic distance and the temporal distance through visualization, selecting a plurality of events that influence the target event and examining a pattern of the plurality of events to realize possible collinearity of the events to further reduce the influencing events in order to facilitate feature selection through visualization.

Abstract: Embodiments of the present invention relate to a system for data integration and information retrieval by bringing semantically related data together for a given context. As described, the integration of data may include the building of an ontology, the mapping of one or more processes, semantic maps and concept dictionaries in the ontology to one or more data sources, tagging the data sources in accordance with the ontology, providing a query interface for accepting an input query from a user, the mapping of the input query to one or more concepts in the ontology, and deriving one or more subqueries thereby, and the querying of data sources in accordance with the composed one or more subqueries, wherein the data sources queried are tagged with one or more concepts from the ontology. Additionally, the tracking of data across data sources in accordance with a defined data value chain is disclosed.

Abstract: This disclosure describes a system and methodology for sharing, analyzing and consolidating medical data on social networks in a well secured manner. It allows patients, medical professionals, and caregivers participate in an open media without the fear of their personal information being compromised. At the same time, it allows each user to benefit from analysis of clinical information available on network. It facilitates sharing of information with chosen contacts in a specific manner such that the visibility of information can be configured on individual basis. More importantly, it facilitates the creation of authentic source of medical database at the backend that can be made available for research and analytics on preserving privacy.