Natural Language Query Mapping to Knowledge Graph Nodes
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Solution Overview
Problem
Conversational user interfaces struggle to provide answers to 'long-tail' questions, which are infrequently encountered, as existing models often return lists of articles instead of relevant answers, failing to map user queries to specific nodes in a knowledge graph.
Innovation Solution
A method that extracts operands and operators from natural language queries using machine learning models, mapping them to nodes and operations in a knowledge graph to generate query results, allowing for the execution of operations on data within the graph and returning direct answers to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional conversational models are used to match user questions to answers, then frequently encountered questions can be answered, but long-tail questions return only suggested articles without relevant answers
Solution Approach 1:
The patent segments the natural language query into distinct components: intent, operands, and operators. This segmentation allows the system to process different parts of the query separately and map them to corresponding elements in the knowledge graph, enabling accurate answers for both common and long-tail questions.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates natural language queries into structured operations on the knowledge graph. This intermediary layer includes components that extract intent, identify operands, determine operators, and execute queries, serving as a bridge between user input and knowledge graph data.
2Adaptability or versatility
If exhaustive node mappings are implemented to cover all possible questions, then complete coverage is achieved, but system complexity increases significantly
Solution Approach 1:
The patent implements a universal query processing framework that can handle diverse question types through a common set of operations. The system uses generic intent extraction, operand identification, and operator determination mechanisms that work across different domains and question types, eliminating the need for domain-specific mappings.
Solution Approach 2:
The patent changes the approach from static exhaustive mappings to dynamic parameter-based querying. Instead of pre-defining mappings for all possible questions, the system extracts parameters (operands) from the query and uses them to dynamically construct and execute queries on the knowledge graph, adapting to any question type.
3Ease of operation
If natural language queries are processed without extracting operands and operators, then processing is simpler, but the ability to perform operations on knowledge graph data is lost
Solution Approach 1:
The patent performs preliminary actions by extracting intent, operands, and operators from the natural language query before executing the knowledge graph query. This preliminary processing step prepares the query in a structured format that can be efficiently executed on the knowledge graph, maintaining simplicity while enabling powerful operations.
Solution Approach 2:
The patent replaces manual or mechanical query construction with automated natural language processing. The system automatically extracts operands and operators from unstructured natural language input and translates them into structured query operations, eliminating the need for manual query formulation while maintaining execution capability.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for processing natural language utterances in a knowledge graph. An example method generally includes receiving a long-tail query comprising a natural language utterance from a user of an application. Operands and operators are extracted from the natural language utterance using a natural language model. Operands may be mapped to nodes in a knowledge graph, the nodes representing values calculated from data input into the application, and operators may be mapped to operations to be performed on data extracted from the knowledge graph. The functions associated with the operators are executed using data extracted from the nodes in the knowledge graph associated with the operands to generate a query result. The query result is returned as a response to the received long-tail query.