Knowledge Graph Data Update via Schema Comparison

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Solution Overview

Problem

Existing database updating methods fail to efficiently identify and fill missing information, leading to incomplete or outdated data, as they lack effective mechanisms for automatically retrieving and integrating new data from external sources.

Innovation Solution

A system that identifies missing data elements in a knowledge graph by comparing entity references to schema tables and generates natural language queries to a query processing engine, such as a search engine or question answering service, to retrieve and update the missing information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional database updating methods are used, then data updating can be performed, but missing information cannot be efficiently identified and filled, leading to incomplete or outdated data

Engineering Contradiction:

Improvedata accuracyVSAvoidmissing information

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The system uses schema tables to define expected data elements and compares them against actual database contents to identify missing information. This feedback mechanism automatically detects gaps and triggers query generation to retrieve missing data from external sources, ensuring data completeness and accuracy without manual intervention

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The database system automatically identifies its own missing information by comparing entity references against schema definitions, generates appropriate queries, and updates itself using retrieved data. This self-service capability eliminates the need for manual data auditing and updating, continuously maintaining data reliability

Inventive Principle:

Principle #25Self-service

2Productivity

If manual data updating is performed, then data can be modified, but the process is time-consuming and inefficient

Engineering Contradiction:

Improvedata updating efficiencyVSAvoidtime for identifying and filling missing data

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system replaces manual mechanical processes of data auditing and updating with automated computational processes. Algorithms automatically compare database contents against schema definitions, generate appropriate queries, retrieve missing data, and update records without human intervention, dramatically improving productivity and eliminating time loss associated with manual operations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces schema tables as an intermediary layer between the database and external data sources. This intermediary automatically identifies missing elements and generates appropriate queries to external sources, streamlining the data updating process and eliminating the need for manual identification and retrieval of missing information

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If no automated mechanism is used, then system complexity is low, but data cannot be automatically retrieved and updated from external sources

Engineering Contradiction:

Improveability to retrieve and integrate new dataVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements a multi-functional automated framework that combines schema validation, missing data identification, query generation, data retrieval, and database updating into a single integrated mechanism. This universal system handles various data types and sources while maintaining a consistent automated process, enhancing adaptability without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10108700B2Question answering to populate knowledge base

Publication Date: 2018.10.23 GOOGLE LLC

AI summary

Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.