Automated Knowledge Validation Using Deterministic Finite Automata
Solution Overview
Problem
Existing systems lack an efficient method for validating structured data, particularly in business trading networks, where invalid information can compromise data quality and consistency, especially due to manual injection and diverse sources.
Innovation Solution
A system and method using deterministic finite automata for content-based knowledge validation, creating a semantic model to represent facts and their dependencies, and generating a deterministic finite automaton for validating facts against a Consolidated Model, enabling automatic and configurable validation of structured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual injection and diverse sources are used for data collection, then data quantity and coverage are improved, but data quality and consistency deteriorate
Solution Approach 1:
The patent applies preliminary action by performing validation before data is fully integrated into the system. A semantic model is created in advance that defines expected patterns and constraints, and a determinist finite automaton is generated to automatically check incoming data against these predefined rules, preventing invalid data from compromising the knowledge base
Solution Approach 2:
The patent introduces an intermediary validation layer between data collection and data integration. The semantic model acts as a mediator that translates business rules into executable validation logic, while the determinist finite automaton serves as an intermediary tool that systematically applies these rules to filter and validate data from diverse sources before it enters the consolidated knowledge base
2Reliability
If automatic validation is implemented, then data quality is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex manual validation processes with an automated computational system. Instead of relying on manual checking or complex rule-based systems, it uses a determinist finite automaton - a well-defined mathematical model with clear states and transitions - to automatically validate data against the semantic model, reducing both manual effort and system complexity
Solution Approach 2:
The patent changes the parameter of validation from manual or heuristic-based approaches to formal language theory-based automated validation. By transforming validation rules into a determinist finite automaton with defined states, transitions, and acceptance criteria, the system achieves automated validation with predictable and manageable complexity
Data Source
AI summary
A computer implemented method for knowledge validation includes identifying a fact for validation. A semantic model representing the fact for validation can be created. A context associated with the fact can be identified, and an automaton can be created based at least in part on the identified context. The fact can be validated using the automaton.