Multi-Focus Query Annotation in Question Answering Systems
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Solution Overview
Problem
Conventional question answering systems are inadequate in handling queries that require identification of multiple entities in a relationship, as they are designed to focus on a single entity and fail to provide comprehensive answers to questions like "What assassins are associated with politicians in the United States," where the focus on a single entity leaves ambiguity about the roles of individuals in the relationship.
Innovation Solution
The method involves parsing queries to generate an instance graph, providing constraint graphs organized into ranks, and annotating the query with the focus of the highest-ranking matching constraint graphs, enabling the identification and processing of multiple foci in the query, which allows for improved answer retrieval with higher confidence and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional factoid QA system focuses on a single entity, then the system structure remains simple, but the system cannot sufficiently answer queries requiring identification of multiple entities in a relationship
Solution Approach 1:
The patent segments the query processing into multiple independent constraint graphs, each focused on identifying a specific entity type (assassin, politician, etc.). This allows the system to handle multiple entity identification tasks separately and then integrate the results, resolving the contradiction between handling complex multi-entity queries and maintaining manageable system structure.
Solution Approach 2:
The patent introduces a new dimension of constraint graph ranking and matching, transforming the single-entity focus into a multi-entity framework by organizing constraint graphs into hierarchical ranks. This dimensional expansion enables the system to process multiple entity types simultaneously while maintaining structural organization.
2Ease of operation
If the QA system uses natural language queries instead of database query languages, then ease of use improves, but the system cannot perform complex relationship queries without a structured database
Solution Approach 1:
The patent introduces constraint graphs as an intermediary representation layer between natural language queries and database operations. The instance graph parser converts natural language into a structured instance graph, which then matches against constraint graphs to extract precise relationship information, preserving query precision while maintaining natural language input simplicity.
Solution Approach 2:
The patent replaces the traditional mechanical database query language interface with a natural language processing system that uses instance graph parsing and constraint graph matching. This substitution maintains the precision of structured queries while eliminating the need for users to learn complex query languages.
3Measurement precision
If the system annotates queries with multiple foci, then answer accuracy and comprehensiveness improve, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary annotation of queries with multiple foci during the parsing stage, before the actual answer retrieval process. By pre-identifying and annotating all relevant entity types and their relationships in the instance graph, the system prepares the query structure in advance, reducing the complexity of subsequent processing steps.
Solution Approach 2:
The patent implements a dynamic constraint graph matching process where the system adapts the number and type of foci based on the specific query structure. The ranking mechanism dynamically selects which constraint graphs to apply, allowing the system to handle varying levels of complexity flexibly without a fixed processing overhead.
Data Source
AI summary
A method of augmenting an information retrieval method including receiving a query, parsing the query to generate an instance graph, providing a plurality of constraint graphs, organized into at least two ranks, each constraint graph associated with a focus, determining at least one of the constraint graphs matching a structure of the instance graph, and annotating the query with the focus of each matching constraint graph of a highest rank among all matching constraint graphs.