Much of this can be found on the Web. I have tried to consolidate the most important points and of course added my two cents. I would suggest anyone that believes the Big Data trends needs to understand the role Ontology will play going forward.
In computer science and information science, an ontology formally represents knowledge as a hierarchy of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts.
Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework.
As it relates to the Big Data trend:
Ontology claims to be to applications what Google was to the web. Instead of integrating the many different enterprise applications within an organization to obtain, for example, a 360 degrees view of customers, Ontology enables users to search a schematic model of all data within the applications. They extract relevant data from a source application, such as a CRM system, big data applications, files, warranty documents etc. These extracted semantics are linked into a search graph instead of a schema to give users the results needed.
Ontology gives users a different approach in using enterprise applications, removing the need to integrate the different applications. It allows users to search and link applications, databases, files, spreadsheets, etc. anywhere. The product of Ontology is very interesting because in the past years a vast amount of enterprise applications for various needs and with various requirements have been developed and used by organizations. Integrating these applications to obtain a company-wide integrated view is difficult, expensive and often not without risks.
Why is it important?
It eliminates the need to integrate systems and applications when looking for critical data or trends.
How is it applied and what are the important elements that make it all work?
Ontology uses a unique combination of an inherently agile, graph-based semantic model and semantic search to reduce the timescale and cost of complex data integration challenges. Ontology is rethinking data acquisition, data correlation and data migration projects in a post-Google world.
No comments:
Post a Comment