Semantic Knowledge Graph for Query Autocomplete
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Solution Overview
Problem
Business intelligence systems face challenges in providing efficient query completion and insights due to the complexity of raw data and lack of effective context, leading to suboptimal user experience and data utilization.
Innovation Solution
The implementation of a method and system for automatic query completion using natural language processing and semantic knowledge graphs, where event logs are parsed to identify objects and relationships, generating a semantic knowledge graph that suggests autocomplete options based on weighted relationships, enhancing query execution and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional business intelligence systems are used for query completion, then system simplicity is maintained, but query efficiency and user experience deteriorate due to lack of effective context and autocomplete suggestions
Solution Approach 1:
The system pre-parses event logs and pre-generates the semantic knowledge graph before query execution. This preliminary processing creates a ready-to-use contextual framework that enables fast autocomplete suggestions during actual query operations, improving query efficiency without adding complexity during critical moments
Solution Approach 2:
A semantic knowledge graph is introduced as an intermediary layer between raw event logs and the query system. This knowledge graph captures contextual relationships and serves as a mediator that provides relevant suggestions to users, enhancing query efficiency while keeping the core system architecture relatively simple
2Adaptability or versatility
If raw data is provided without context, then data storage simplicity is maintained, but data utilization and insight generation deteriorate
Solution Approach 1:
Event logs are pre-parsed to extract objects and relationships before they are needed for query execution. This preliminary action creates a structured semantic knowledge graph that enriches raw data with context, enabling better data utilization without requiring complex processing during actual data operations
Solution Approach 2:
The system automatically parses event logs and generates the semantic knowledge graph without requiring manual intervention. This self-service approach enriches data with contextual relationships autonomously, improving data utilization while avoiding the complexity of manual data structuring
3Measurement precision
If manual query construction is used, then system resource consumption is minimized, but user experience and query accuracy deteriorate
Solution Approach 1:
Instead of processing all possible data relationships, the system provides partial autocomplete suggestions based on the most relevant contextual relationships in the semantic knowledge graph. This partial action approach improves query accuracy by offering targeted suggestions while avoiding the computational overhead of exhaustive analysis
Solution Approach 2:
The semantic knowledge graph acts as an intermediary that pre-computes and stores contextual relationships. This intermediary structure enables the system to quickly retrieve relevant suggestions without performing complex real-time computations, thereby improving query accuracy while minimizing computational resource consumption during query operations
Data Source
AI summary
A system and method for automatically completing queries. The method includes parsing a textual input into a plurality of first query objects; determining a plurality of scores based on a semantic knowledge graph including a plurality of query nodes, wherein each query node corresponds to a respective second query object of a plurality of second query objects, wherein each query node is connected by an edge to another query node of the plurality of query nodes, wherein each edge represents a relationship between the corresponding second query objects of the respective query nodes, wherein each edge is associated with a score representing a relationship between a first query node and a second query node of the plurality of query nodes; and generating an autocomplete suggestion notification based on the plurality of scores, wherein the autocomplete suggestion notification includes at least one second query object of the plurality of query objects.