Dynamic Topic Graph for Question Answering Systems
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Solution Overview
Problem
Existing topic-oriented question answer systems rely on static topic definitions based on document structure, limiting their ability to provide clear and concise answers to questions, as they fail to dynamically combine relevant topics based on semantic relations.
Innovation Solution
A method and system that dynamically define topics by creating a graph of nodes representing topics, assigning edge weights based on semantic relations, and combining topics into supertopics when edge weights exceed a threshold, allowing for the generation of more relevant answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static topic definitions based on document structure are used, then the system structure is simple and easy to implement, but the system cannot dynamically combine relevant topics based on semantic relations, reducing answer relevance and accuracy
Solution Approach 1:
The patent transforms static topic definitions into dynamic topic structures by introducing graph-based representations where topics are nodes and semantic relations are edges. The system dynamically combines topics into supertopics based on calculated edge weights that reflect semantic strength, allowing the topic structure to adapt flexibly to different questions and content objects while maintaining manageable complexity through algorithmic automation.
Solution Approach 2:
The patent introduces graph structures as an intermediary layer between raw content objects and question answering. The graph nodes represent topics and edges represent semantic relations, serving as a mediator that captures complex semantic relationships without requiring direct complex processing between all content elements, thus improving adaptability while controlling system complexity.
2Reliability
If multiple separate topics are returned as candidate answers, then the system maintains topic granularity and precision, but the answers may be fragmented and less coherent
Solution Approach 1:
The patent merges closely related topics into supertopics based on edge weight thresholds that indicate strong semantic relationships. This combining process maintains topic granularity for unrelated concepts while creating coherent supertopics for related concepts, preventing information loss by preserving the underlying topic structure within supertopics and restoring it when needed.
Solution Approach 2:
The patent uses edge weight parameters to dynamically control topic combination. By adjusting the threshold parameter, the system can flexibly control the degree of topic merging, allowing it to maintain granularity when needed (higher threshold) or improve coherence (lower threshold) based on specific requirements, thus balancing reliability and information preservation.
3Measurement precision
If the system uses detailed semantic analysis to combine topics, then the answer relevance improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-building graph structures from content objects before question answering. The graph nodes and edges representing topics and their semantic relations are established in advance, allowing the system to quickly query and combine relevant topics during actual question answering without performing full semantic analysis each time, thus improving precision while reducing processing time.
Solution Approach 2:
The patent replaces complex mechanical semantic analysis processes with graph-based computational methods. Instead of performing detailed semantic analysis for every topic combination query, the system uses pre-calculated edge weights and graph traversal algorithms to efficiently determine topic relationships, substituting intensive computational mechanics with more efficient graph theory-based operations.
Data Source
AI summary
According to one exemplary embodiment, a method for dynamically defining topics from content objects used to answer a question in a question answering system is provided. The method may include receiving the content objects. The method may include identifying the topics within the received content objects. The method may include generating a graph based on the identified plurality of topics, whereby nodes map to the topics. The method may include calculating edge weights associated with each edge based on semantic relations associated with the topics. The method may include determining if the calculated edge weight associated an edge exceeds a threshold value. The method may include generating a combination topic based on determining that the edge weight associated with the edge exceeds the threshold value.