Modular Reasoning System for Insider Threat Detection
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Solution Overview
Problem
Current reasoning systems face challenges in efficiently processing and classifying information across diverse domains, particularly in detecting insider threats, as they struggle to effectively utilize semantic graphs and ontologies to infer new knowledge and classify abstractions.
Innovation Solution
An information processing system comprising a working memory with semantic graphs and a reasoning system with modular architecture, where reasoning modules access and process abstractions based on domain-specific ontologies, applying rules to classify and infer new information, and reifiers instantiate data into appropriate formats for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reasoning systems use semantic graphs and ontologies to process information across diverse domains, then the ability to infer new knowledge and classify abstractions is improved, but the system complexity increases
Solution Approach 1:
The reasoning system is divided into multiple independent reasoning modules, each specialized for processing specific domains or types of information. Each module contains its own ontology and reasoning components, allowing the system to handle diverse domains without requiring a monolithic complex structure. This segmentation enables modular development and easier maintenance while maintaining high adaptability.
Solution Approach 2:
The system employs a universal reasoning framework that can accommodate multiple domain-specific ontologies and reasoning modules. The framework provides common infrastructure for semantic graph management, knowledge representation, and inference mechanisms that serve all domains. This allows a single system architecture to handle diverse information processing tasks across multiple domains effectively.
2Reliability
If reasoning systems process and classify information across multiple domains, then the detection capability for complex patterns is improved, but the processing time increases
Solution Approach 1:
By dividing the reasoning system into specialized modules, each responsible for specific domain knowledge and classification tasks, the system can process information in parallel across different domains. This segmentation reduces the processing time for complex multi-domain patterns while maintaining high detection capability, as each module operates independently on its designated domain data.
Solution Approach 2:
The system pre-processes and organizes domain-specific ontologies and knowledge graphs before actual reasoning operations. By having ready-access structured knowledge representations and pre-indexed semantic graphs, the reasoning modules can quickly infer and classify new information without time-consuming on-the-fly processing, thus reducing overall processing time while maintaining reliable detection.
3Manufacturing precision
If the system uses domain-specific ontologies for each reasoning module, then the classification precision is improved, but the system configuration complexity increases
Solution Approach 1:
Each reasoning module is configured with its own domain-specific ontology, allowing high classification precision for that particular domain. The segmentation of ontologies by domain ensures that each module operates with optimized, domain-relevant knowledge without interference from other domains, thereby achieving precise classification while keeping individual module configurations manageable and independent.
Solution Approach 2:
The system provides mechanisms for dynamic configuration and adjustment of ontology parameters, such as weighting, hierarchy levels, and inference rules, allowing fine-tuning of classification precision for each domain without reconfiguring the entire system. This parameter-based adjustment approach maintains high precision while reducing overall configuration complexity by localizing changes to specific modules rather than requiring system-wide reconfiguration.
Data Source
AI summary
Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.