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EQ KGNN (kajun), the world’s first Knowledge Graph Neural Network (KGNN) platform reimagines the landscape of data unification by infusing traditional knowledge graphs with advanced neural networks, propelling enterprises into a new era of intelligent data analysis.

EQ Kajun is the solution engineered to comprehend, reason, and dynamically interact with complex data ecosystems, setting a new standard for actionable insights.

Kajun KG PLATFORM

✓ Ingests most known forms, types, and sources of data
✓ Utilizes advanced proprietary machine learning models to identify correlations and establish/record explicit relationships

✓ Automatically merges duplicate entities when data sources have overlapping information, reducing storage requirements
✓ Facilitates the seamless ingestion, transformation, querying, storage, and analysis of data across the organization
✓ Open architecture frameworks works with both legacy and next-generation applications

Key Benefits:

  • Automated learning and adaptation
  • Dynamic reasoning for relationship insights
  • Real-time graph updates
  • Accelerated data integration
  • Enhanced data quality and consistency
  • Personalized recommendations
  • Predictive analytics
  • Complex query handling
  • Highly scalable architecture

Kajun (KGNN) provides a unified solution that addresses the historical limitations of knowledge graphs. Its ability to perform real-time learning and reasoning presents a transformative opportunity for enterprises seeking to harness their data’s full potential. This innovation is the key to unlocking a world where data comprehension and reasoning are not just concepts but tangible assets that drive decision-making and strategic foresight.

Equitus AI’s integration of a neural network with a knowledge graph represents a groundbreaking development. By fusing these two technologies, Equitus AI not only addresses the challenges of data unification with a more advanced approach but also overcomes the inherent limitations that each technology exhibits when operating independently.

Knowledge graphs alone are adept at organizing data and showing the relationships between entities, but they can be static and may not adapt quickly to new information. Neural networks, on the other hand, excel at learning from data patterns and making inferences, but they can struggle with the interpretability and structured organization of the data they process.
The integration of neural networks with knowledge graphs allows for a system that benefits from the strengths of both.

EQ AI OpenFabric solves these challenges through:

  • Automated ontology evolution
  • Continuous self-updating capabilities
  • Parallel real-time data processing

 

Designed to propagate and learn from both the structure and data within the graph, EQ Kajun (KGNN) enables human-like comprehension and reasoning across datasets. This ensures that the generated knowledge is not merely a random collection of facts but rather a coherent, understandable, and actionable set of insights.

By combining the strengths of knowledge graphs and neural networks, EQ AI OpenFabric unlocks new possibilities:

  • Predict unseen relationships and future trends
  • Gain situational insights from context
  • Ask natural language questions
  • Handle complex analytical tasks
  • Continuously learn from users and data