Knowledge Graphs

Knowledge graphs are data structures, representing knowledge of the real world including entities(people, companies, digital assets, etc.) and their relationships, which adheres to a graph data model— a network of nodes (vertices) and links (edges/arcs). The knowledge within the graph can beexplicitly stated or implicitly inferred using rules that are defi ned in an ontology for classes of entitiesand relationships. Further knowledge can be derived using graph analytics and machine learning.

Graphs links can be Direct or Infered, funcitonal or semantic. As such the focus shoudl be on eing able to process data in a new way that prepares it for graph-based navigation. In other words, Infernce Query first, Knowledge Graph after. Knowledge Graph technology enables you to represent real-world knowledge and link it to a broad variety of data sources. It focuses on the connected nature of data and its context.

What Problem Does This Solve?

Knowledge graphs capture information about the world in an intuitive way that is often easier to understand, manipulate and use than other types of data models. Google, Facebook, Amazon and other tech companies use graphs as the backbone of a number of products and services due to their ability to encode and interrelate disparate data at source. They support collaboration and sharing, search and discovery, and the extraction of insights through analysis. Layers atop the existing data infrastructure that “reveal the relationships within the data, regardless of the source or format.”

Graph links may be direct or inferred based on Semagraph Indexes. Explains Latent or direct Semantic Attribution..

Knowledge graphs can drive business impact in a variety of different settings including: Drivers Digital workplace (e.g., collaboration, sharing and insight). ■ Automation (e.g., ingestion of data from content to RPA). ■ Machine learning (e.g., augmenting training data). ■ Investigative analysis (e.g., law enforcement, cybersecurity or financial transactions). ■ Digital commerce (e.g., product information management and recommendations). ■ Data management (e.g., metadata management, data cataloging and data fabric).

A data fabric is a modern data architecture that accelerates new and emerging business use cases such as customer 360, customer intelligence, fraud detection, and advanced analytics. To best enable the data fabric, a semantic model is necessary to associate all related data, source agnostic, with a common language any person can understand. The knowledge graph blends data models and semantics to make all data and content readily consumable.


1. Combine Disparate Data Silos

2. Bring Together Structured and Unstructured Data

3. Make Better Decisions by Finding Things Faster


  • Extensibility: The ability to accommodate diverse data and metadata that evolve over time.
  • Introspection/Query Ability: Models that can be inspected to find what things are knowable and findable.
  • Semantic: The meaning of the data is stored within the graph alongside the data to understand connections.
  • Intelligence Enabling: The ability to infer dependencies and other relationships between objects.


Graph technology enables you to represent knowledge and link it to data 

Graph technology emphasizes the connected nature of data

use a data fabric to support data-intensive tasks including machine learning and data analysis 

A a data fabric supports intense data-driven business initiatives more robustly than a simple database or data architecture 


smart multilateral relations throughout your databases. Structured as an additional virtual data layer, the Knowledge Graph lies on top of your existing databases or data sets to link all your data together at scale – be it structured or unstructured.. a Semantic Middleware Layer that represents interconnected objects.. This ‘sits’ on the database and at the same time offers service endpoints for integration with third-party systems.


Pharmaceutical Industry

One of the top 20 companies in the pharmaceutical industry uses the extensive capabilities of Enterprise Knowledge Graphs to provide a unified view of all their research activities.


A global telecom company benefits from the power of Enterprise Knowledge Graphs, helping to generate chatbots based on semi-structured documents.

A large IT services enterprise uses Enterprise Knowledge Graphs to help them link all unstructured (legal) documents to their structured data; helping the enterprise to intelligently evaluate risks that are often hidden in common legal documents in an automated manner.


A large governmental organization provides trusted health information for their citizens by using several standard industry Knowledge Graphs (such as MeSH and DBPedia etc.). The governmental health platform links more than 200 trusted medical information sources that help to enrich search results and provide accurate answers

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