Ambiguous Entity Verification Using Relationship-Aware Answer Validation
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Solution Overview
Problem
Current document processing systems struggle to accurately tag and understand ambiguous entities, establish their relationships, and provide contextually relevant answers due to a lack of sophistication and robust entity relationship mechanisms, leading to misinterpretations and suboptimal query results.
Innovation Solution
A system comprising processors to tag entities, create relationships among them, generate answers with confidence scores, and validate using k-similar answers and similarity scores, ensuring accurate and context-aware responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional document processing systems are used to tag and understand entities, then the system complexity remains low, but the accuracy and reliability of entity understanding deteriorates due to inability to handle ambiguous entities
Solution Approach 1:
The system segments the document processing task into distinct modules: entity tagging module, relationship extraction module, query processing module, and validation module. Each module handles specific aspects of entity processing independently, allowing the system to manage complexity through modular decomposition while improving overall reliability through specialized processing at each stage.
Solution Approach 2:
The system performs preliminary actions by first tagging all entities in the document, then establishing relationships between them before processing user queries. This preliminary structuring of entity information creates a ready-to-query knowledge graph that improves query accuracy without adding complexity during the query processing itself.
2Ease of operation
If current document processing systems lack robust entity relationship mechanisms, then the system remains simple, but the ability to provide contextually relevant answers deteriorates
Solution Approach 1:
The system merges multiple processing functions into a unified pipeline where entity tagging, relationship extraction, and query processing operate together. The relationship extraction module combines information from multiple entities and their relationships to generate comprehensive answers, improving contextual relevance through integrated processing rather than separate isolated functions.
Solution Approach 2:
The system introduces an intermediary knowledge graph structure that mediates between the raw document and the final answers. This intermediate representation stores entity relationships and contextual information, allowing the query processing module to access relevant contextual data without directly parsing the entire document, thus improving answer relevance while managing complexity through the intermediary layer.
3Reliability
If users receive answers without validation against similar answers, then the response time is fast, but the accuracy and reliability of the answers deteriorates
Solution Approach 1:
The system implements feedback by retrieving k similar answers to the query and using them to validate the generated answer. The validation module compares the generated answer against these similar answers and adjusts the confidence score accordingly. This feedback mechanism improves answer accuracy by cross-checking against alternative possibilities while the efficiency of the validation process is maintained through optimized similarity searching and confidence thresholding.
Data Source
AI summary
A system for verifying ambiguous entities in documents. The system comprises a processor configured to tag at least one entity in at least one page of a document, create an entity relationship among the tagged entities of each of the at least one page, receive a question from a user for the document, generate at least one corresponding answer for said question, generate an embedded question and an embedded corresponding answer for said question and each of the at least one corresponding answer, respectively, retrieve k-similar answers with associated labels for each of the at least one corresponding embedded answer for said question, and validate each of the corresponding at least one embedded answer if the labels associated with the embedded question, the corresponding at least one embedded answer, and each of the k-similar answers match.