Core Concepts

This page explains the fundamental concepts behind Qwello's knowledge processing capabilities.

Knowledg Graphs Explained

What is a Knowledge Graph?

A knowledge graph is a structured representation of knowledge that consists of:

• Entities: The "things" or concepts in the domain (e.g., people, organizations, concepts, locations)

• Relationships: The connections between entities that describe how they relate to each other

• Attributes: Properties that provide additional information about entities and relationships

Knowledge graphs represent information in a way that captures semantic meaning and context, making it possible to understand not just individual facts but how they relate to each other.

Knowledge Graph Structure in Qwello

Qwello generates knowledge graphs with the following structure:

{
  "entities": [
    {
      "id": "e1",
      "type": "concept",
      "name": "Artificial Intelligence",
      "attributes": {
        "description": "The simulation of human intelligence in machines",
        "mentioned_on_pages": [1, 4, 7],
        "importance": "high"
      }
    },
    {
      "id": "e2",
      "type": "person",
      "name": "Alan Turing",
      "attributes": {
        "description": "British mathematician and computer scientist",
        "mentioned_on_pages": [2],
        "role": "pioneer"
      }
    }
  ],
  "relationships": [
    {
      "source": "e2",
      "target": "e1",
      "type": "contributed_to",
      "attributes": {
        "description": "Developed foundational concepts for AI",
        "mentioned_on_pages": [2],
        "significance": "major"
      }
    }
  ]
}

Benefits of Knowledge Graphs

Knowledge graphs offer several advantages over traditional document processing:

1. Contextual Understanding: Capture relationships between concepts, not just the concepts themselves

2. Semantic Representation: Represent meaning, not just text

3. Flexible Querying: Enable complex queries that traverse relationships

4. Integration Capability: Easily combine information from multiple sources

5. Inference Support: Allow for logical reasoning and inference

6. Visual Representation: Provide intuitive visualization of complex information

Entity Resolution and Relationship Mapping

Entity Resolution

Entity resolution is the process of identifying when different mentions in a document refer to the same real-world entity. This is a critical capability in Qwello that ensures the knowledge graph accurately represents the document's content.

Challenges in Entity Resolution

Entity resolution addresses several challenges:

1. Name Variations: The same entity might be referred to by different names (e.g., "IBM" and "International Business Machines")

2. Acronyms: Entities might be referred to by acronyms (e.g., "AI" for "Artificial Intelligence")

3. Partial References: Entities might be referred to by partial names (e.g., "Turing" instead of "Alan Turing")

4. Ambiguity: Different entities might share the same name (e.g., "Apple" the company vs. "apple" the fruit)

5. Cross-Page References: The same entity might be mentioned on different pages with different contexts

Entity Resolution Process in Qwello

Qwello's entity resolution process involves several steps:

1. Name Matching: Compare entity names for exact or fuzzy matches

2. Acronym Resolution: Recognize acronyms and their expanded forms

3. Contextual Analysis: Use surrounding context to disambiguate similar entities

4. Attribute Comparison: Compare entity attributes for similarity

5. Reference Pattern Analysis: Analyze how entities are referenced throughout the document

When entities are resolved as referring to the same concept, their attributes and relationships are merged, creating a more comprehensive representation of the entity.

Relationship Mapping

Relationship mapping is the process of identifying and categorizing connections between entities. This process transforms isolated facts into a connected network of knowledge.

Types of Relationships

Qwello identifies various types of relationships:

1. Hierarchical: Parent-child, superclass-subclass relationships (e.g., "is a type of")

2. Associative: General connections between entities (e.g., "is related to")

3. Temporal: Time-based relationships (e.g., "happened before", "happened after")

4. Causal: Cause-effect relationships (e.g., "causes", "results in")

5. Spatial: Location-based relationships (e.g., "is located in", "is near")

6. Functional: Relationships based on function or purpose (e.g., "is used for")

7. Compositional: Part-whole relationships (e.g., "is part of", "contains")

Relationship Extraction Process

Qwello extracts relationships through several steps:

1. Co-occurrence Analysis: Identify entities that appear together in the same context

2. Linguistic Pattern Recognition: Detect language patterns that indicate relationships

3. Semantic Analysis: Understand the meaning of the text to infer relationships

4. Contextual Understanding: Use surrounding context to determine relationship types

5. Cross-Reference Analysis: Identify relationships mentioned in multiple places

Relationship Attributes

Relationships in Qwello can have attributes that provide additional context:

1. Description: Textual description of the relationship

2. Page References: Pages where the relationship is mentioned

3. Confidence Score: Measure of certainty about the relationship

4. Temporal Context: When the relationship is/was valid

5. Qualifiers: Additional information that qualifies the relationship

Knowledge Graph Applications

Knowledge graphs enable various applications in Qwello:

1. Document Exploration

• Navigate through document content based on concepts and relationships

• Discover connections that might be separated by many pages in the original document

• Focus on specific entity types or relationship types

2. Question Answering

• Answer complex questions about document content

• Traverse relationships to find indirect connections

• Provide evidence for answers with page references

3. Insight Generation

• Identify key concepts and their relationships

• Discover patterns and trends across documents

• Generate summaries and reports based on graph analysis

4. Knowledge Integration

• Combine information from multiple documents

• Identify contradictions or confirmations across sources

• Build comprehensive knowledge bases from document collections

Understanding these core concepts provides the foundation for effectively using Qwello's knowledge processing capabilities. As you continue exploring the documentation, you'll see how these concepts are implemented in practice and how they enable powerful document analysis and knowledge discovery.