Knowledge Graph Comparison for Narrative Inconsistency Detection
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Solution Overview
Problem
Inconsistencies in narrative universes across multiple works of authorship, such as books or movies, can be difficult to detect and correct, especially when they involve fictional settings, requiring extensive reference to source material and often going unnoticed until they manifest as factual inaccuracies or conflicts within the new work.
Innovation Solution
A method utilizing knowledge graphs generated through natural language processing to compare concepts and relationships in a target work against a background of associated works, identifying potential inconsistencies by matching and overlaying nodes and edges representing characters, themes, and actions, and notifying authors for corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reference to source material is used to detect inconsistencies, then detection accuracy can be high, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical review of source materials with an automated computational system that uses natural language processing and knowledge graphs to detect inconsistencies. The system automatically extracts entities, relationships, and events from texts, builds knowledge graphs, and compares them to identify contradictions without human intervention, thereby maintaining detection accuracy while dramatically reducing time consumption.
Solution Approach 2:
The patent creates structured copies of narrative content in the form of knowledge graphs that replicate the semantic relationships present in the original texts. By copying and structuring information from source materials into machine-readable formats with entities, relationships, and events, the system enables automated comparison and inconsistency detection without requiring manual reference to the original documents.
2Reliability
If extensive reference to source material is made to ensure narrative consistency, then accuracy improves, but the complexity of the verification process increases
Solution Approach 1:
The patent segments the complex verification process into distinct modular components: text ingestion, entity extraction, relationship extraction, event extraction, knowledge graph construction, and inconsistency detection. Each module handles a specific aspect of the verification process, making the overall system more manageable and less complex while maintaining comprehensive narrative consistency checking across multiple works.
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary representation layer between the original narrative texts and the inconsistency detection logic. The knowledge graphs serve as a structured medium that captures entities, relationships, and events in a standardized format, facilitating automated comparison and reducing the complexity of directly analyzing unstructured text for inconsistencies.
3Measurement precision
If manual verification of inconsistencies is performed, then nuanced understanding can be achieved, but productivity decreases
Solution Approach 1:
The patent replaces manual verification with automated natural language processing algorithms that analyze narrative texts and compare knowledge graphs. The system uses computational methods to identify entities, relationships, and events, and automatically detects inconsistencies such as temporal contradictions, factual errors, and narrative conflicts, maintaining high identification quality while processing multiple works simultaneously to greatly increase productivity.
Solution Approach 2:
The patent performs preliminary extraction and structuring of narrative elements into knowledge graphs before inconsistency detection. By pre-processing the texts to create structured representations of entities, relationships, and events, the system prepares the data in advance for efficient automated comparison, enabling both high-quality inconsistency identification and increased processing throughput without requiring manual verification of each detail.
Data Source
AI summary
A processor obtains a target knowledge graph that includes target nodes that represent concepts used within a target work and target edges between target nodes that represent links used within the target work to associate the concepts used therein with each other. The processor also obtains a background knowledge graph that includes background nodes that represent concepts used within a background work and background edges between background nodes that represent links used within the background work to associate the concepts used therein with each other. The processor compares a portion of the target knowledge graph to a portion of the background knowledge graph. Based on the comparison, the processor identifies a potential inconsistency between the background work and the target work.