- Artificial Intelligence for Big Data
- Anand Deshpande Manish Kumar
- 268字
- 2021-06-25 21:57:07
Ontology of information science
Formally, the Ontology of information sciences is defined as: A formal naming and definition of types, properties, and interrelationships of the entities that fundamentally exist for a particular domain.
There is a fundamental difference between people and computers when it comes to dealing with information. For computers, information is available in the form of strings whereas for humans, the information is available in the form of things. Let's understand the difference between strings and things. When we add metadata to a string, it becomes a thing. Metadata is data about data (the string in this case) or contextual information about data. The idea is to convert the data into knowledge. The following illustration gives us a good idea about how to convert data into knowledge:
The text or the number 66 is Data; in itself, 66 does not convey any meaning. When we say 660 F, 66 becomes a measure of temperature and at this point it represents some Information. When we say 660 F in New York on 3rd October 2017 at 8:00 PM, it becomes Knowledge. When contextual information is added to Data and Information, it becomes Knowledge.
In the quest to derive knowledge from data and information, Ontologies play a major role in standardizing the worldview by precisely defined terms that can be communicated between people and software applications. They create a shared understanding of objects and their relationships within and across domains. Typically, there are schematic, structural, and semantic differences, and hence conflict arises between knowledge representations. Well-defined and governed Ontologies bridge the gaps between the representations.
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