Axis-Lattice Query Language for Unified Knowledge Retrieval
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Solution Overview
Problem
Conventional information systems face challenges in constructing a unified system that efficiently stores data, executes graph and logical queries, and provides concise context to artificial-intelligence models due to fragmented schema and logic across different data models, leading to duplication of facts, high run-time costs for inverse-relationship traversals, and brittleness in data pipelines.
Innovation Solution
An axis-lattice query language paradigm and deterministic compiler framework that unify the semantics of tables and graphs, allowing for efficient storage and retrieval of knowledge using a rectilinear query language, reducing token count and computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data models (relational, graph, vector stores) are used separately, then each can address specific workload requirements, but schema and logic remain fragmented across different data models leading to duplication of facts and high run-time costs
Solution Approach 1:
The patent merges multiple data model capabilities into a single unified graph database system. It combines relational table operations, graph traversal, and vector embedding storage within one system, eliminating the need to maintain separate data stores for different workloads. The unified system uses a single schema language and query interface that can handle all these operations, thereby reducing schema fragmentation while maintaining adaptability to various workload requirements.
Solution Approach 2:
The patent creates a universal graph database system that performs multiple functions previously requiring separate systems. The same graph database can execute relational queries, perform graph traversals, store and retrieve vector embeddings, and maintain structured data all through a unified interface and schema system. This multi-functionality eliminates the complexity of managing multiple specialized data models while maintaining versatility across different workload types.
2Productivity
If multiple data models are used to handle different workloads, then specific workload requirements are met, but equivalent facts are duplicated in multiple grammatical or directional forms
Solution Approach 1:
The patent merges the storage and representation of equivalent facts into a single unified form within the graph database. Instead of duplicating facts across relational tables, graph edges, and vector stores in separate systems, the unified system represents all these in a single graph structure with a unified schema, eliminating redundant fact representations while maintaining the ability to handle different workload types through the same data structure.
3Ease of operation
If conventional query languages and data pipelines are used, then data can be stored and retrieved, but data pipelines that move information between symbolic knowledge structures and sub-symbolic embeddings introduce brittleness and latency
Solution Approach 1:
The patent merges the storage and processing of symbolic knowledge structures and sub-symbolic embeddings into a single unified graph database system. By co-locating structured data, graph relationships, and vector embeddings in one system with a unified query interface, it eliminates the need for complex data pipelines that transfer information between separate symbolic and sub-symbolic systems. This unified architecture reduces pipeline brittleness and latency while maintaining ease of data storage and retrieval operations.
Data Source
AI summary
Computer-implemented techniques for representing, storing, retrieving, and reasoning over knowledge are disclosed herein. One aspect is an axis-lattice query language paradigm that turns every query string into a rectangular Matrix→Vector→Limit→Data tensor whose shape is dictated by token position. The invention improves the operations of a computing device by increasing the amount of significant information manipulated per unit of time by a processor.