It’s the connections and the graph that make the knowledge graph, not the language used to represent the data. A key feature of a knowledge graph is that entity descriptions should be interlinked to one another. The definition of one entity includes another entity. This linking is how the graph forms (e.g., A is B; B is C; C has D; A has D). Knowledge bases without formal structure and semantics, e.g., Q&A “knowledge base” about a software product, also do not represent a knowledge graph. It is malaysia whatsapp number data possible to have an expert system that has a collection of data organized in a format that is not a graph but uses automated deductive processes such as a set of “if-then” rules to facilitate analysis.
Knowledge graphs are not software either. Rather a knowledge graph is a way to organize and collect the data and metadata to meet criteria and serve specific purposes which, in turn, is used by different software. The data of one knowledge graph can be used in multiple independent systems for different purposes.
The demands on our data have pushed traditional approaches to data management past their limits. There are copious amounts of data, more every day, and it all needs to be processed, understood, and made useful. It needs to be reliable and done in real-time regardless if it is coming from internal or external sources. After all, the value of data depends wholly on the ability to leverage its use.