Discover what a knowledge graph is, how it works and how it can sort and streamline information, making data more accessible across your organization.
What is a knowledge graph?
Table of contents:
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What is a knowledge graph?
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What are the benefits of a knowledge graph?
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How do knowledge graphs work?
A knowledge graph is a graph structure that visualizes the relations between interlinked entities representing real-world objects and concepts — such as people, places or even organizational structures.
The true value of a knowledge graph lies in the semantic knowledge model that can be layered within it—or what we also refer to as an ontology. We refer to a knowledge graph powered by a semantic model as a “semantic knowledge graph”.
The semantic model enhances the knowledge graph and turns it into a powerful tool that transforms data into knowledge. By introducing formal, explicit definitions of the concepts and relations within a domain, it enriches the knowledge graph and the data it holds.
Similar to a legend found in a paper map, a semantic model adds richness and context to data within a knowledge model. Without a legend, a map is simply an array of colors and symbols rather than a tool guiding you toward your destination. Similarly, using a knowledge graph within your organization can provide critical context for your data, offering you a roadmap for sound decision-making and business success.
What are the benefits of a knowledge graph?
Most organizations lack a centralized platform through which employees can readily access internal data, making it challenging to acquire the necessary data to complete tasks or projects.
Skyrocketing data silos and valuable domain expertise are often left unrecorded or forgotten, turning the simple task of finding specific data points into an overwhelming process.
Employees must traverse multiple systems and applications, attend countless meetings or scan hundreds of emails or paper documents just to collect the right information.
Having a semantic knowledge graph at the core of your organization’s data infrastructure offers several benefits, including:
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Streamlining and centralizing knowledge from multiple sources & systems in one place, making vital information more easily and readily available.
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Advanced data analysis: When contextual information and semantic meaning are added, data transforms into knowledge. This lays the foundation for surfacing hidden connections and extracting valuable insights from your data.
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Fosters collaboration & improves organization-wide data literacy: It promotes collaboration by making data consumable for all relevant stakeholders across the organization
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Data & knowledge reusability: Extends data for future use cases or powering intelligent applications by making it interpretable to both humans and machines
How do knowledge graphs work?
To understand how knowledge graphs add value in the workplace, let’s revisit Ray’s example from the video above.
As a data analyst for a luxury vehicle and motorcycle manufacturer, she’s often responsible for finding information about:
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the types of vehicles produced,
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which suppliers were used, or
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how many vehicles were sold in a specific region during a given timeframe
To access this information, Ray must navigate hundreds of individual systems and applications and manually compile this data. At times, she’s uncertain of the systems or applications to search through and has to rely on the experience of colleagues like Rob, who has been with the company for longer.
However, Rob isn’t of much help because he and Ray don’t speak the same language—there is a semantic gap between them. Meaning, that they lack a shared understanding of the terminology or concepts used within the organization, as a result of factors such as their specific departments or industry experience.
A knowledge graph, however, can enable Ray’s organization to
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bridge the semantic gap by enabling machine-interpretable, explicit semantics across the entire data value chain,
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drive more informed decisions and encourage innovation,
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and drive the organization’s strategy away from application-centricity and towards data-centricity.
Big tech companies have used knowledge graphs successfully for over a decade to power their search engines, social media platforms, or virtual assistants. For Ray’s organization, knowledge graphs make information more easily accessible via a uniform, centralized user interface, enabling Ray and her colleagues to finally find all the answers they’re looking for.
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Knowledge graph use cases: