Query Language for Semi-Structured Data Fact Repository
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Solution Overview
Problem
Current search technologies are inadequate for efficiently querying large collections of semi-structured data, as users often lack knowledge of the specific attributes and formats within these datasets, making it difficult to construct effective queries.
Innovation Solution
A system and methodology for searching semi-structured data using a query language that allows users to query without knowing all attribute names, incorporating components like importers, janitors, and a service engine to process and manage facts within a fact repository, enabling querying across diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users query structured databases using SQL with predefined fields, then search precision is improved, but ease of operation deteriorates because users must know the specific attribute names and formats
Solution Approach 1:
The patent introduces an intermediary layer between the user and the structured database. This intermediary automatically extracts entities, attributes, and relationships from unstructured natural language queries, transforming them into structured search criteria without requiring users to know database schemas or attribute names. The system mediates between the simplicity of natural language and the precision of structured queries.
Solution Approach 2:
The system performs self-service by automatically generating search queries from natural language input. The patent implements automated entity recognition, attribute extraction, and query construction mechanisms that enable the system to serve itself in translating user intent into precise database queries without human intervention in the transformation process.
2Ease of operation
If users search unstructured data using simple keyword search, then ease of operation is improved, but search precision deteriorates because users cannot effectively filter or specify search criteria
Solution Approach 1:
The patent applies preliminary action by pre-processing and structuring unstructured data before the search phase. The system performs entity recognition, relationship extraction, and attribute identification in advance, organizing unstructured data into a semi-structured format that enables precise querying while maintaining ease of use. This preliminary structuring allows the system to offer both simplicity and precision.
Solution Approach 2:
The system dynamically changes parameters by automatically adjusting search criteria based on the extracted entities and relationships from natural language queries. The patent implements mechanisms that transform simple keyword matches into parameterized searches with multiple filtering dimensions, enabling precise search results while keeping the user interface simple.
3Adaptability or versatility
If the system stores data in semi-structured format with diverse formats and sources, then adaptability is improved, but device complexity increases due to the need for importers, janitors, and service engines
Solution Approach 1:
The patent segments the complex data processing system into distinct functional modules: importers for data ingestion, janitors for data cleaning and normalization, and service engines for query processing. This segmentation allows each component to handle specific tasks independently, managing complexity through modular architecture while maintaining high adaptability to diverse data sources.
Solution Approach 2:
The system implements universal components that can handle multiple data formats and sources. The importers, janitors, and service engines are designed with multi-functional capabilities to process various semi-structured data types (JSON, XML, CSV, etc.) from diverse sources, reducing overall system complexity through standardized, versatile components rather than specialized handlers for each data type.
Data Source
AI summary
A fact repository supports searches of facts relevant to search queries comprising keywords and phrases. A service engine retrieves the objects that are associated with facts relevant to a query. The query language described is designed for use with such a repository of facts and searches both the attributes of facts and the values of the attributes.