# Core Concepts **Learn the fundamental concepts behind Semantica in simple, practical terms.** !!! tip "Quick Start" New to Semantica? Start with [Getting Started](getting-started.md) for hands-on examples. --- ## What is Semantica? Semantica transforms unstructured data (documents, web pages, reports) into **knowledge graphs** - structured databases that AI systems can understand and reason about. **What it does:** - **Reads** documents, PDFs, web pages, databases - **Extracts** entities (people, companies, dates) and relationships - **Builds** connected knowledge graphs - **Enables** AI to reason with structured knowledge --- ## Core Architecture Semantica uses a **layered architecture** - use only what you need:
- **Input Layer** --- Data ingestion and preparation **Modules**: Ingest, Parse, Split, Normalize - **Semantic Layer** --- Intelligence and understanding **Modules**: Semantic Extract, Knowledge Graph, Ontology, Reasoning - **Storage Layer** --- Persistent data storage **Modules**: Embeddings, Vector Store, Graph Store - **Quality Layer** --- Data quality and consistency **Modules**: Deduplication, Conflicts - **Context & Memory** --- Agent memory and foundation data **Modules**: Context, Seed, LLM Providers - **Output & Orchestration** --- Export, visualization, and workflows **Modules**: Export, Visualization, Pipeline
--- ## Knowledge Graphs The foundation of Semantica - turning data into structured knowledge. ### What is a Knowledge Graph? A knowledge graph represents real-world information as: - **Nodes** (entities): People, companies, locations, dates - **Edges** (relationships): works_for, located_in, founded_by - **Properties**: Name, date, confidence score, source ### Why Knowledge Graphs? - **Searchable**: Find information instantly - **Connectable**: Discover hidden relationships - **Queryable**: Ask complex questions - **Explainable**: Trace answers back to sources --- ## Entity Extraction (NER) Finding and classifying entities in text. ### What it does: - Scans text for people, organizations, locations, dates - Classifies each entity by type - Assigns confidence scores - Tracks source provenance ### Example Output: ```python # From: "Apple Inc. was founded by Steve Jobs in 1976 in Cupertino." { "entities": [ {"text": "Apple Inc.", "type": "ORGANIZATION", "confidence": 0.98}, {"text": "Steve Jobs", "type": "PERSON", "confidence": 0.99}, {"text": "1976", "type": "DATE", "confidence": 0.95}, {"text": "Cupertino", "type": "LOCATION", "confidence": 0.97} ] } ``` --- ## Relationship Extraction Finding connections between entities. ### What it does: - Identifies how entities relate to each other - Extracts relationship types and directions - Provides context and confidence - Links to source documents ### Example Output: ```python { "relationships": [ {"subject": "Steve Jobs", "predicate": "founded", "object": "Apple Inc.", "confidence": 0.92}, {"subject": "Apple Inc.", "predicate": "located_in", "object": "Cupertino", "confidence": 0.89} ] } ``` --- ## Embeddings Turning text into numerical vectors for AI understanding. ### What are embeddings? - **Numerical representations** of text, entities, and relationships - **Similarity calculations** - find related concepts - **AI-powered search** - semantic understanding - **Clustering and grouping** - discover patterns ### Use Cases: - **Semantic Search** - find documents by meaning, not keywords - **Entity Resolution** - match similar entities across sources - **Recommendations** - suggest related content - **AI Input** - provide structured context to LLMs --- ## Temporal Graphs Knowledge graphs that understand time. ### What they track: - **When** events happened - **How** entities changed over time - **Temporal relationships** - before, after, during - **Historical context** - point-in-time snapshots ### Example Uses: - **Company History** - track mergers, leadership changes - **Person Careers** - job changes, relocations - **Policy Evolution** - law changes over time - **Research Progress** - scientific discoveries timeline --- ## GraphRAG Enhanced AI retrieval using knowledge graphs. ### How it works: 1. **Query** user question 2. **Retrieve** relevant graph context 3. **Enhance** with relationships and entities 4. **Generate** AI response with sources ### Benefits: - **More accurate** answers - **Source attribution** - trace answers back - **Context awareness** - understand relationships - **Reduced hallucination** - grounded in facts --- ## Ontology Defining the structure and rules of your knowledge. ### What it provides: - **Schema definition** - what types exist - **Relationship rules** - valid connections - **Property constraints** - required fields - **Inheritance hierarchies** - parent-child relationships ### Example: ```python # Define ontology structure ontology = { "classes": ["Person", "Organization", "Location"], "properties": ["name", "date", "confidence"], "relationships": ["works_for", "located_in", "born_in"], "rules": { "Person": ["must_have_name", "can_have_birth_date"], "Organization": ["must_have_name", "can_have_founding_date"] } } ``` --- ## Reasoning & Inference Making logical deductions from your knowledge. ### What it can do: - **Infer missing facts** - derive new knowledge - **Detect inconsistencies** - find contradictions - **Apply rules** - automate decision making - **Explain reasoning** - show how conclusions were reached ### Example: ``` Known: Steve Jobs founded Apple Inc. Known: Apple Inc. is headquartered in Cupertino Inferred: Steve Jobs has connection to Cupertino ``` --- ## Deduplication & Entity Resolution Finding and merging duplicate entities. ### What it does: - **Detects duplicates** - same entity, different names - **Merges information** - combine attributes - **Resolves conflicts** - handle contradictory data - **Maintains provenance** - track original sources ### Example: ```python # These refer to the same entity: "Apple Inc." → "Apple" → "Apple Computer Inc." # Merge into single entity with all attributes ``` --- ## Data Normalization Cleaning and standardizing your data. ### What it fixes: - **Format inconsistencies** - dates, names, numbers - **Canonical forms** - standard representations - **Data quality** - remove errors and noise - **Standardization** - consistent naming conventions ### Examples: - **Dates**: "Jan 1, 2020" → "2020-01-01" - **Names**: "Dr. Smith PhD" → "John Smith" - **Companies**: "Apple" → "Apple Inc." - **Locations**: "NYC" → "New York City" --- ## Conflict Detection Finding and resolving contradictory information. ### What it identifies: - **Factual conflicts** - different values for same fact - **Temporal conflicts** - impossible timelines - **Logical conflicts** - contradictory relationships - **Source reliability** - trustworthiness assessment ### Resolution Strategies: - **Most recent** - prefer newer information - **Most reliable** - prefer trusted sources - **Majority vote** - go with consensus - **Manual review** - flag for human review --- ## Getting Started Ready to build your first knowledge graph? ### Quick Start (5 minutes) ```python from semantica.semantic_extract import NERExtractor from semantica.kg import GraphBuilder # Extract entities ner = NERExtractor() entities = ner.extract("Apple Inc. was founded by Steve Jobs in 1976.") # Build graph kg = GraphBuilder().build({"entities": entities, "relationships": []}) ``` ### Learn More - **Getting Started Guide** - [Getting Started](getting-started.md) - **Cookbook Examples** - [Cookbook](cookbook.md) - **Module Documentation** - [Reference](reference/) - **Community Support** - [Community](community.md) ### Common Use Cases - **Document Analysis** - extract knowledge from reports - **Research Assistant** - find connections in academic papers - **Business Intelligence** - analyze company relationships - **Regulatory Compliance** - track policy changes --- ## Best Practices ### Start Small - Begin with a single document type - Focus on specific entity types - Validate results before scaling ### Configure Properly - Choose appropriate models for your domain - Set confidence thresholds - Define clear ontology rules ### Validate Data - Check extraction quality - Review relationship accuracy - Test with known examples ### Handle Errors - Implement error handling - Log processing issues - Provide feedback mechanisms ### Optimize Performance - Use appropriate storage backends - Cache frequently accessed data - Monitor resource usage ### Document Workflows - Record processing steps - Track data sources - Maintain change logs --- ## Need Help? - **Documentation**: [Getting Started](getting-started.md) - **Examples**: [Cookbook](cookbook.md) - **Community**: [Discord](community.md) - **Issues**: [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues) - **Support**: [Contact Us](community.md)