Entity Context Graph Generation for Knowledge Base Automation
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Solution Overview
Problem
The creation and maintenance of knowledge graphs (KGs) are time-consuming and costly, and current methods struggle to capture and represent complex relationships between entities, leading to incomplete and outdated information that hinders intelligent search and retrieval systems.
Innovation Solution
The system automatically generates an entity context graph from textual descriptions, extracting context triples to learn entity-relationship embeddings without relying on traditional KGs, enabling dynamic representation and updating of relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional knowledge graphs are manually curated and maintained, then entity relationships can be structured and stored, but the process becomes time-consuming and costly
Solution Approach 1:
The system enables automatic entity relationship extraction from text data without manual curation. The entity graph generation process autonomously identifies entities, extracts relationships, and structures data into triples, allowing the system to self-populate knowledge representations without human intervention while maintaining relationship accuracy
Solution Approach 2:
The patent replaces the manual mechanical process of KG curation with automated computational processes. Natural language processing algorithms substitute human analysts, automatically parsing text to extract entity relationships and converting them into structured graph representations, thereby eliminating time-consuming manual work
2Device complexity
If fixed set of short labels are used to define relationships in KG, then data structure is simplified, but complex relationships cannot be captured
Solution Approach 1:
The patent segments relationship representation into multiple components: the primary relationship type (short label) and additional context attributes (descriptive text, metadata). This segmentation allows the system to maintain simple structured triples while capturing complex relationship nuances through additional segmented information layers
Solution Approach 2:
The patent implements nested representation where context attributes are nested within the relationship structure. The entity graph stores triples with embedded context information, allowing short labels to provide structural organization while nested context attributes contain detailed relationship descriptions, creating a multi-layered representation that preserves both simplicity and complexity
3Reliability
If manual curation is used to maintain KG, then data quality can be controlled, but the system cannot adapt to frequently changing data
Solution Approach 1:
The patent implements dynamic entity graph generation that automatically adapts to changing data. The system continuously processes new text data, extracts updated entity relationships, and refreshes the knowledge graph structure in real-time, enabling the system to dynamically adapt to frequently changing information while maintaining quality through automated validation processes
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously evaluates extracted entity relationships against existing graph structures. Automated quality checks and consistency validations provide feedback loops that maintain data quality standards while allowing the graph to evolve with new information, balancing quality control with adaptability
4Loss of information
If complex relationships are captured in triple format, then entity connections are represented, but the process becomes difficult and requires significant attention
Solution Approach 1:
The patent implements a universal entity graph framework that handles multiple relationship types through a single unified structure. The triple format (entity, relationship, entity) serves as a universal container that can represent simple and complex relationships alike, with additional context attributes providing the necessary detail for complex cases without requiring separate representation mechanisms
Data Source
AI summary
A method, an electronic device and computer readable medium for entity-relationship embeddings using automatically generated entity graphs instead of a traditional knowledge graph are provided. The method includes receiving, by a processor, an input text. The method also includes identifying a primary entity, a secondary entity and a context from the input text, wherein the context comprises a relationship between the primary entity and the secondary entity. The method additionally includes generating, by the processor, an entity context graph based on the primary entity, the secondary entity, and the context by: extracting, from the context, one or more text segments comprising a plurality of words describing one or more additional relationships between the primary entity and the secondary entity, and generating a plurality of context triples from the one or more text segments, each of the plurality of context triples defining a respective relationship between primary entity and the secondary entity.