Semantic Query Engine for Disparate Data Sources
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Solution Overview
Problem
Current systems for retrieving information from disparate data sets are inefficient, requiring individual querying of each resource and often resulting in ambiguous and costly summaries, as they lack a unified approach to handle various data types and contexts.
Innovation Solution
A data processing system comprising a knowledge manager, canonical model manager, and context manager that processes natural language queries by mapping them to domain models, identifying relationships, and managing context to position resources for efficient information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If individual querying of each disparate resource is performed, then complete information retrieval is achieved, but system complexity and time consumption increase significantly
Solution Approach 1:
The patent introduces a semantic query engine as an intermediary layer between the user and disparate data sources. This engine translates natural language queries into logical form, maps them to canonical models representing different data types (relational databases, XML documents, spreadsheets, etc.), and coordinates querying across multiple resources. The intermediary handles the complexity of interfacing with diverse systems, allowing users to query all resources through a unified interface without manually managing each data source's specific query language or structure.
2Loss of information
If individual querying of each disparate resource is performed, then complete information retrieval is achieved, but time consumption increases significantly
Solution Approach 1:
The patent implements preliminary action by pre-defining canonical models that represent the structure and query interfaces of various data sources. These canonical models are prepared in advance, containing information about how to access and query different resource types. When a user submits a query, the system maps the query to these pre-prepared models rather than needing to analyze and adapt to each data source's specific interface in real-time, significantly reducing query processing time.
3Ease of operation
If natural language queries are processed without domain models, then ease of use is improved, but measurement precision and result accuracy deteriorate due to ambiguity
Solution Approach 1:
The patent transforms the query from natural language to logical form, changing the parameter representation from ambiguous human language to precise structured data. The system uses domain models to map natural language terms to specific concepts and relationships in the data, ensuring that the query semantics are preserved and accurately reflected in the results. This parameter transformation maintains ease of use by accepting natural language input while improving precision through structured logical representation.
4Productivity
If context management is not implemented, then device complexity is reduced, but information discovery effectiveness deteriorates due to inability to align information with user context
Solution Approach 1:
The patent segments the context management function into distinct components: the context manager handles user context and query state, while the canonical model manager handles data structure definitions. This segmentation allows context management to be implemented without creating a monolithic complex system, as each component has a specific responsibility and can be independently managed and optimized.
Data Source
AI summary
The different advantageous embodiments provide a system for positioning data within a network comprising a knowledge manager, a canonical model manager, and a context manager. The knowledge manager is configured to process a query across a number of resources to generate a result. The canonical model manager includes a number of models used to identify relationships between types of information within the number of resources and the query. The context manager is configured to manage the context of the query and the relationships identified by the canonical model manager to position the number of resources for access by the knowledge manager.