Knowledge Graph


A knowledge graph (KG) organizes data from various sources, gets information on entities in a specific domain (e.g., people or places), and creates connections between them in order for the data to be made understandable by the computer. The machine then retains this information as knowledge, using it at different times and in a variety of different circumstances.

Typical characteristics of knowledge are that it is situational, layered, and changing:

  • Situational refers to the fact that knowledge continuously changes depending on the situation 
  • Knowledge can be complex and contain many nuances; by being layered and creating associations between notions, these nuances become understandable.
  • When uncovering new information, previous meanings can suddenly change.

Knowledge Graph: Key Components 

A knowledge graph is made up of the following elements:

Ontology: the design in which knowledge is organized within the graph is mainly dependent on its ontology. Ontology defines the categories of assets (e.g., people), their properties (e.g., names), and their relationships. Ontologies are the backbone of the semantic of a knowledge graph, ensuring a consistent understanding between the data and its meaning.

Vocabulary: Knowledge graphs continuously collect vocabulary in order to make it possible for it to recognize and understand concepts used to describe specific resources. 

Reasoning: reasoning is used to deduce new information from the graph’s knowledge. By doing so, the graph can be used to complete external data and continuously ensure a consistent and complete level of information for a variety of services and applications. 

Relational Systems Vs. Knowledge Graphs 

Traditional 'relational systems' operate by storing data in tables and rely on rigid processes that remain unchanged over time. This approach deems these conventional systems unequipped to capture the key factors that encompass real knowledge (as described above), thus being the reason for why context is often missing from within data.   

Contrary to this, knowledge graphs organize data in a specific way, defining a domain by linking the relationships it finds between data points, instead of relying on fixed relationships defined in a relational system. This structure enables knowledge graphs to overcome a variety of issues and adapt to the dynamic environment knowledge defines itself as. 

The Importance of Flexibility  

Knowledge graph's unique data-driven approach is not the only aspect that makes them distinguishably more effective than relational systems; their flexibility is another significant component to their success.

Being reliant on fixed processes means that inserting data into the system requires a substantial amount of time and added complexity, since each entity needs to undergo a permanent data transformation to maintain uniformity to the existing data model. Although this approach has its benefits, mainly by offering a unified view of the data, it does not provide any flexibility. This factor limits how accurate and trustworthy the data is, as it cannot represent the situational, layered, or changing nature of reality.

On the other hand, a knowledge graph can take real-world dynamics and convert them into a context that machines can easily understand. Knowledge graphs can carry out such adaptations by linking entities in a flexible manner instead of integrating data into a fixed structure. All this is done without altering the infrastructure of a system based on a knowledge graph.

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Conversational AI
Digital Transformation
Natural Language
Knowledge Graph
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