Document Graph Reranking for Hidden Semantic Connections
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Solution Overview
Problem
Existing methods for Open-Domain Question Answering (ODQA) fail to effectively capture connections between documents, leading to the disregard of relevant documents with less apparent connections to the question context, and incur high computational costs due to redundant information integration.
Innovation Solution
A reranking method using graph neural networks constructs document graphs where nodes represent documents and edges represent shared concepts, selectively encoding connection information through message-passing mechanisms to identify relevant documents, reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If methods integrate AMR graphs into learning models to deal with complex semantic information, then the ability to capture semantic connections is improved, but computational time and GPU memory cost increase
Solution Approach 1:
The patent extracts and integrates AMR graph structures into the learning model architecture, separating the semantic representation function from the main computational pipeline. By using AMR graphs to pre-structure semantic relationships before feeding data to the neural network, the system captures complex semantic connections more effectively while reducing the computational burden during training and inference.
Solution Approach 2:
The patent performs preliminary processing by generating AMR graphs and extracting semantic relationships before the main learning process. This preliminary action pre-structures the semantic information, allowing the model to focus computational resources on learning patterns rather than parsing semantic structures during training, thus reducing overall computational time while maintaining semantic accuracy.
2Measurement precision
If methods integrate AMR graphs into learning models to deal with complex semantic information, then the ability to capture semantic connections is improved, but GPU memory cost increases
Solution Approach 1:
The patent extracts essential semantic features from AMR graphs and integrates only the necessary structural information into the learning model, rather than processing complete AMR graphs. This selective extraction reduces memory requirements while preserving the ability to capture complex semantic connections through carefully chosen graph features and relationships.
Solution Approach 2:
The patent segments the AMR graph processing into manageable components, processing different parts of the graph structure separately and integrating them progressively. This segmentation allows efficient memory utilization by handling graph nodes and edges in discrete steps rather than loading the entire graph structure into memory simultaneously.
3Measurement precision
If reranking methods use graph neural networks to capture connections between documents, then identification of relevant documents is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by using graph neural networks selectively only for the reranking stage, rather than applying them throughout the entire document processing pipeline. This approach captures document connections effectively where needed while avoiding unnecessary computational overhead in earlier retrieval stages, thus improving relevance identification with controlled computational cost.
Solution Approach 2:
The patent applies graph neural networks with local quality by tailoring the graph structure and message-passing mechanisms specifically to the reranking task requirements. The graph construction and GNN parameters are optimized for capturing document-level connections rather than general-purpose processing, reducing computational overhead by avoiding unnecessary complexity while maintaining effective connection capture.
Data Source
AI summary
A computer-implemented method includes: in response to receiving a query, retrieving a plurality of documents; generating a first graph representation which graphically represents the plurality of documents utilizing a plurality of nodes indicating a concept and a plurality of edges indicating a relationship between at least two nodes among the plurality of nodes; generating a second graph representation with respect to the plurality of documents, based on connection information associated with the first graph representation; ranking the plurality of documents, based on the second graph representation; and applying one or more machine-learned models to generate a response to the query based on the plurality of documents ranked based on the second graph representation.