Knowledge Graph -Part 2

Effective business decision-making requires organizations to constantly collect, process, and act on tons of data. Yet, the accuracy and timeliness of decisions are dismal. And it’s not the lack of data that is a reason for this. It is the inability to find connections and unearth links that make it difficult to understand associations and build relationships. ?That’s where ??knowledge graphs come in!??

Is it similar to Decision Tree?

A decision tree ??can be defined as a flowchart-like structure representing attributes, their potential decisions and outcomes for each decision. Knowledge graphs is a much ??a viable alternative to decisions trees when modelling complex conversation flows and complex data connections.

Despite the knowledge graph’s intricacy, it often gives better explanations than basic pies and charts. Unlike pyramids and ladder illustrations, ??knowledge graphs need little to no interpretation ??as the words are directly connected to the boxes provided in the graph.?

What is the difference between Knowledge Graph and regular database?

Although a regular database is occasionally populated, it largely ? remains dormant. The knowledge graph is dynamic and ??ever-changing. It keeps re-purposing itself to provide new insights and inferences to its users. Since a knowledge graph is depicted graphically, users can easily extend and revise the ontology (a formal description of knowledge as a set of concepts within a domain) when new data arrives. Moreover, a knowledge graph understands what connects entities?? on its own??This eliminates the need to manually program new data ??every single time.

Here is a list of key differences between a knowledge graph and a?regular database.

How does ontology differ from knowledge graphs?

An ontology serves to formally represent the nodes in a graph. It is a model or subset that lists the types of entities, their interconnected relationships, and the limitations in combining entities and their relationships. It essentially lays down the ??rules that govern the relationships among various entities.

Ontologies?represent the backbone of the formal semantics of a knowledge graph. They can be seen as the data schema of the graph???They serve as a formal contract between the developers of the knowledge graph and its users regarding the meaning of the data in it. A user could be another human being or a software application that wants to interpret the data in a reliable and precise way. Ontologies ensure a shared understanding of the data and its meanings.

In the example above, Shoe Ontology?(dotted grey box) represent the backbone of the formal semantics of the given knowledge graph. Note the inferences made!

A knowledge graph understands what connects entities?? on its own??This eliminates the need to manually program new data ??every single time.

Since knowledge graphs and ontologies are represented in a similar manner—i.e. through nodes and edges—and are based on the ??Resource Description Framework (RDF) triples, they tend to resemble each other in visualizations.