Hierarchical Knowledge Graph Summaries for Domain QA Context Retrieval
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Solution Overview
Problem
Existing natural language generation systems, particularly in question-answering (QA) systems, face challenges due to high computational resource requirements, limited domain-specific information, and inefficiencies in retrieving relevant information, leading to suboptimal answer quality and resource consumption.
Innovation Solution
A knowledge graph incorporating textual summaries is generated to provide improved input context to a language model, utilizing a tree structure with document summaries at the root and textual summaries at leaf nodes, enabling efficient retrieval and generation of answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a knowledge graph with textual summaries is used to provide input context to a language model, then the quality of answers is improved by enhancing retrieval of relevant information, but the complexity of the system increases due to the need to generate and maintain the knowledge graph structure
Solution Approach 1:
The knowledge graph is generated in advance with textual summaries at multiple levels (document summaries at root nodes, section summaries at intermediate nodes, and passage summaries at leaf nodes). This preliminary structuring of information allows the language model to efficiently retrieve relevant context without requiring complex real-time processing, thereby improving answer quality while managing system complexity through pre-computation
Solution Approach 2:
The knowledge graph acts as an intermediary between the raw document corpus and the language model. By introducing this structured intermediate representation with hierarchical summaries, the system bridges the gap between unstructured text and the language model's processing requirements, enhancing information retrieval effectiveness without directly modifying the language model itself
2Reliability
If multiple textual passages and summaries are retrieved and provided as input to a language model, then the quality of answers is improved, but the computational resource usage increases
Solution Approach 1:
The system extracts only the most relevant textual passages and their associated summaries from the knowledge graph based on the input question. By selectively retrieving a limited set of high-value information units (passages and summaries) rather than processing entire documents or all available text, the system improves answer quality while reducing computational resource consumption
Solution Approach 2:
The knowledge graph divides documents into hierarchical segments (documents → sections → passages) with summaries at each level. This segmentation allows the system to retrieve only the necessary granular level of information required to answer a question, avoiding the computational overhead of processing complete documents while maintaining answer quality through targeted information selection
3Reliability
If a tree structure knowledge graph with document summaries at root and textual summaries at leaf nodes is used, then the retrieval of relevant information is enhanced, but the memory resource requirements increase
Solution Approach 1:
The knowledge graph implements a nested hierarchical structure where document summaries at root nodes contain references to section summaries, which in turn contain references to passage summaries at leaf nodes. This nesting allows the system to store comprehensive information in a compact form where each level summarizes and references the levels below it, enhancing information retrieval effectiveness while optimizing memory usage through hierarchical compression
Data Source
AI summary
Techniques relate to receiving a natural-language textual sequence representing the textual input; retrieving, from a knowledge graph associated with the domain: a first textual passage based on a first ranking regarding the natural-language textual sequence, a first textual summary associated with the first textual passage, a second textual passage based on a first ranking with respect to a comparison of the second textual passage and the natural-language textual sequence a second textual summary associated with the second textual passage, and a third textual summary based on a second ranking regarding the natural-language textual sequence; obtaining the textual output in response to the textual input from a language model by providing input to the language model using the natural-language textual sequence, the first textual passage, the first textual summary, the second textual passage, the second textual summary, and the third textual summary; and providing an output based on the textual output.