Ontology-Guided Data Classification for Low-Latency AI Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing artificial intelligence systems struggle to manage and process the exponential growth of structured and unstructured data effectively, leading to potential loss of control over what they know, which can be dangerous and destructive.
Innovation Solution
A system and method that maps incoming data to a hierarchical ontology, allows user modification, trains statistical models, and classifies data using machine learning algorithms, with real-time processing and low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management systems are used to store and process data, then data storage capacity is maintained, but data processing speed and throughput deteriorate as data volume grows exponentially
Solution Approach 1:
The patent segments data into structured and unstructured components, processing them through different pathways. Structured data is organized into schemas while unstructured data undergoes entity extraction and classification, allowing parallel processing that maintains speed despite growing data volumes
Solution Approach 2:
The patent adds ontological dimensionality to data by mapping entities to hierarchical ontologies and knowledge graphs. This transforms flat data storage into multi-dimensional knowledge structures, enabling faster querying and processing through semantic relationships rather than brute-force scanning
2Ease of manufacture
If artificial intelligence systems are built with simple architectures, then ease of development is improved, but control and management of knowledge deteriorates leading to potential dangers
Solution Approach 1:
The patent introduces ontologies as intermediary structures between raw data and AI processing. These ontologies serve as controlled vocabularies and hierarchical frameworks that guide AI systems, providing manageability and control while maintaining development simplicity through standardized classification schemes
Solution Approach 2:
The patent implements feedback loops where AI processing results are continuously evaluated against ontological constraints and knowledge graphs. This feedback mechanism ensures that AI systems remain controllable and manageable by comparing outputs against predefined knowledge structures, preventing runaway behavior while preserving development ease
3Loss of information
If real-time data analysis is implemented, then data actionable insight is improved, but system complexity and computational requirements worsen
Solution Approach 1:
The patent performs preliminary actions by pre-building ontologies, knowledge graphs, and classification schemas before data arrives. Entity extraction rules and classification hierarchies are established in advance, allowing real-time data to be quickly mapped to predefined structures without complex runtime processing
Solution Approach 2:
The patent implements dynamic adaptation where the system learns from incoming data and refines ontological mappings in real-time. Classification models are continuously trained and updated, allowing the system to handle increasing data complexity without proportionally increasing overall system complexity through adaptive optimization
Data Source
AI summary
A system and associated methods can be configured to attribute meaning to elements of input information. In one embodiment, the system can map each input datapoint from an incoming stream of data to classes represented in a hierarchical ontology. The ontology can be displayed in a user interface through which a user can make modifications to the ontology, including associating input datapoints with newly defined classes within the ontology's hierarchy. The new associations can then be used to train a statistical model, which in turn can then be used to classify yet additional incoming data. The system can include a control unit and one or more processing units to which the control unit delegates processing tasks. The control unit can also serve as a user/input/output interface. The mapping can be done in real-time using a low latency process.