Knowledge Graph System
Last updated
Last updated
Knowledge graphs represent information as networks of interconnected entities and relationships, enabling sophisticated analysis and querying capabilities that go beyond traditional text search. Qwello generates these graphs automatically from PDF documents, creating rich, explorable representations of document content.
Knowledge graphs use an entity-relationship model to represent information:
Entities represent the key concepts, people, organizations, and objects mentioned in documents:
Unique Identity: Each entity has a unique identifier within the graph
Type Classification: Entities are classified into semantic categories
Descriptive Attributes: Rich metadata provides context and details
Source References: Track which document pages mention each entity
Relationships capture the connections and associations between entities:
Directional Connections: Relationships have source and target entities
Semantic Types: Relationships are classified by their semantic meaning
Contextual Attributes: Additional information about the relationship
Evidence Tracking: References to where relationships are mentioned
Attributes provide detailed information about entities and relationships:
Descriptive Information: Textual descriptions and explanations
Quantitative Data: Numerical values and measurements
Categorical Properties: Classifications and categorizations
Temporal Information: Time-related data and references
The knowledge graph uses a structured format that enables efficient storage, querying, and visualization:
Knowledge Graph Structure:
├── Entities
│ ├── Concepts (ideas, theories, principles)
│ ├── People (individuals, authors, researchers)
│ ├── Organizations (companies, institutions)
│ ├── Locations (places, regions, countries)
│ ├── Technologies (tools, systems, methods)
│ ├── Events (occurrences, milestones)
│ ├── Documents (papers, books, references)
│ └── Products (software, hardware, services)
├── Relationships
│ ├── Hierarchical (includes, part_of, is_a)
│ ├── Associative (related_to, affiliated_with)
│ ├── Temporal (preceded, followed, during)
│ ├── Causal (causes, led_to, enables)
│ ├── Spatial (located_in, near, operates_in)
│ └── Functional (used_for, supports, implements)
└── Attributes
├── Descriptions
├── Properties
├── References
└── MetadataThe system recognizes and classifies entities into semantic categories that provide meaning and enable intelligent filtering:
Abstract Concepts: Ideas, theories, principles, and methodologies
Technical Concepts: Specialized terminology and domain-specific concepts
Academic Concepts: Research topics, fields of study, and academic disciplines
Business Concepts: Strategies, processes, and business methodologies
Individuals: People mentioned in documents with their roles and contributions
Authors: Document authors and their affiliations
Researchers: Scientists, academics, and thought leaders
Professionals: Industry experts and practitioners
Companies: Corporations, startups, and business entities
Institutions: Universities, research institutes, and academic organizations
Government Bodies: Agencies, departments, and regulatory organizations
Non-Profits: Foundations, associations, and charitable organizations
Software Systems: Applications, platforms, and software tools
Hardware: Devices, equipment, and physical systems
Methodologies: Techniques, approaches, and best practices
Standards: Protocols, specifications, and industry standards
Events: Conferences, milestones, and significant occurrences
Time Periods: Eras, phases, and temporal references
Locations: Geographic places, regions, and facilities
Documents: Publications, papers, and reference materials
The system employs intelligent classification that adapts to document content:
Domain Adaptation: Adjust classification based on document domain
Contextual Understanding: Consider surrounding content for accurate classification
Multi-Type Entities: Handle entities that belong to multiple categories
Hierarchical Classification: Support nested and hierarchical entity types
Classification Confidence: Assess certainty of entity type assignments
User Validation: Enable user review and correction of classifications
Learning Integration: Improve classification based on user feedback
The system identifies various types of relationships that capture different aspects of entity connections:
Hierarchical: Parent-child, superclass-subclass relationships
Compositional: Part-whole, component-system relationships
Categorical: Type-instance, classification relationships
Organizational: Reporting, membership, affiliation relationships
Associative: General connections and associations
Functional: Purpose, usage, and application relationships
Causal: Cause-effect, influence, and impact relationships
Comparative: Similarity, difference, and comparison relationships
Sequential: Before-after, precedence relationships
Concurrent: Simultaneous, parallel relationships
Evolutionary: Development, progression relationships
Cyclical: Recurring, periodic relationships
Geographic: Location-based relationships
Proximity: Nearness and distance relationships
Containment: Inside-outside, boundary relationships
Directional: Movement and orientation relationships
Pattern Recognition: Identify common relationship patterns in text
Linguistic Analysis: Use language cues to detect relationships
Context Analysis: Consider surrounding content for relationship validation
Cross-Reference Detection: Identify relationships across document sections
Consistency Checking: Ensure relationships are logically consistent
Evidence Tracking: Maintain references to supporting evidence
Entity resolution ensures that multiple mentions of the same entity are properly unified:
Name Matching: Identify entities with similar or identical names
Alias Recognition: Handle acronyms, abbreviations, and alternative names
Context Comparison: Use contextual information to validate matches
Attribute Correlation: Compare entity attributes for confirmation
Context Analysis: Use surrounding content to distinguish similar entities
Attribute Comparison: Compare entity properties to resolve ambiguity
Relationship Analysis: Use relationship patterns to disambiguate entities
Domain Knowledge: Apply domain-specific rules for disambiguation
Attribute Integration: Combine attributes from multiple entity mentions
Relationship Consolidation: Merge relationships from different sources
Confidence Weighting: Weight information based on source reliability
Cross-Document Entities: Identify entities mentioned across multiple documents
Relationship Bridging: Connect entities from different documents
Knowledge Consolidation: Merge knowledge from multiple sources
Consistency Maintenance: Ensure consistency across integrated graphs
Dynamic Addition: Add new entities and relationships as documents are processed
Relationship Updates: Modify existing relationships based on new information
Entity Enhancement: Enrich existing entities with additional attributes
Graph Evolution: Track changes and evolution of the knowledge graph
Relationship Inference: Derive implicit relationships from explicit ones
Property Propagation: Inherit properties through relationship chains
Pattern Recognition: Identify recurring patterns and structures
Knowledge Completion: Fill gaps in the knowledge graph
Concept Clustering: Group related concepts and entities
Topic Identification: Identify main themes and topics
Importance Ranking: Assess the importance and centrality of entities
Relevance Scoring: Score entities based on their relevance to queries
Consistency Checking: Ensure logical consistency throughout the graph
Completeness Assessment: Identify missing entities and relationships
Accuracy Validation: Verify the accuracy of extracted information
Quality Metrics: Continuously monitor and improve graph quality
Feedback Integration: Incorporate user feedback to improve quality
Error Detection: Automatically detect and flag potential errors
Correction Mechanisms: Provide tools for correcting inaccuracies
Learning Adaptation: Adapt processing based on quality feedback
Visual Navigation: Explore the graph through interactive visualizations
Zoom and Filter: Focus on specific areas or types of entities
Relationship Tracing: Follow relationship paths through the graph
Multi-Level Views: Explore the graph at different levels of detail
Entity Filtering: Show or hide specific types of entities
Relationship Filtering: Focus on particular types of relationships
Layout Options: Choose different visualization layouts and styles
Export Capabilities: Export visualizations in various formats
Intent Recognition: Understand the user's query intent and goals
Entity Identification: Identify entities mentioned in queries
Relationship Traversal: Navigate the graph to find relevant information
Answer Generation: Generate comprehensive, contextual responses
Factual Queries: Direct questions about specific entities or relationships
Exploratory Queries: Open-ended questions for discovery and exploration
Analytical Queries: Questions requiring analysis and reasoning
Comparative Queries: Questions comparing different entities or concepts
Trend Identification: Identify trends and patterns in the knowledge
Anomaly Detection: Discover unusual or unexpected relationships
Cluster Analysis: Find groups of related entities and concepts
Path Analysis: Discover connection paths between entities
Summary Generation: Create summaries of specific topics or areas
Recommendation Systems: Suggest related entities and concepts
Gap Analysis: Identify missing information or knowledge gaps
Impact Analysis: Assess the influence and importance of entities
This knowledge graph system represents a sophisticated approach to knowledge representation and discovery, enabling users to unlock deep insights from their documents through intelligent structuring and interactive exploration of information.