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Mohd KaifandClaude Sonnet 4.6
b282487b17
- Rewrote index.md to match README (tagline, badges, Problem/Solution text) - Improved getting-started, concepts, quickstart, installation, faq, use-cases, contributing, glossary, learning-more, examples, modules, architecture, cookbook, deep-dive pages: tighter prose, fixed headings/bullets, removed inconsistencies and duplicate sections - Removed overuse of emojis from headings in integration pages (docling, snowflake) - Fixed change_management reference page: closed unclosed JSON code block that broke the right TOC, demoted noisy sub-headings to bold text - CSS layout: widened content area (max-width 1440px grid, left sidebar 11rem, right TOC narrowed to 11rem for broader content), tightened TOC spacing and font size, fixed word-wrap/overflow on TOC links - Added mkdocs_local.yml for local serving without mkdocs-jupyter plugin Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
223 lines
7.8 KiB
Markdown
223 lines
7.8 KiB
Markdown
# Glossary
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Reference of terms and concepts used throughout Semantica.
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!!! tip "Finding a term"
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Use Ctrl+F to search this page.
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---
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## Core Concepts
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### **Agent**
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An autonomous AI system that can perceive its environment, reason about information, and take actions to achieve specific goals. In Semantica, agents use knowledge graphs for memory and reasoning.
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### **Entity**
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A distinct object or concept in the real world, such as a person, place, organization, or event. Entities are the fundamental building blocks of knowledge graphs.
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### **Knowledge Graph (KG)**
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A structured representation of knowledge using entities (nodes) and relationships (edges). KGs enable reasoning, querying, and semantic analysis of data.
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### **Relationship**
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A connection between two entities that describes how they relate to each other (e.g., "works_for", "located_in", "founded_by").
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### **Semantic**
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Relating to meaning in language or logic. Semantic understanding goes beyond keywords to comprehend context and intent.
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---
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## Data Processing
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### **Ingestion**
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The process of loading data from various sources (files, databases, APIs, streams) into a system for processing.
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### **Normalization**
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The process of standardizing data into a consistent format (e.g., converting dates to ISO format, standardizing entity names).
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### **Parsing**
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Extracting structured information from unstructured or semi-structured documents like PDFs, Word documents, or web pages.
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### **Chunking**
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Breaking down large documents into smaller, manageable pieces while preserving context and meaning.
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---
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## Artificial Intelligence
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### **LLM (Large Language Model)**
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A type of artificial intelligence model trained on vast amounts of text data, capable of understanding and generating human-like text.
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### **RAG (Retrieval Augmented Generation)**
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A technique that enhances LLM responses by retrieving relevant information from a knowledge base before generating an answer.
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### **GraphRAG (Graph-Augmented Retrieval Augmented Generation)**
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An advanced RAG approach that combines vector search with knowledge graph traversal to provide more accurate and contextually relevant information to LLMs.
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### **Inference**
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The process of deriving new facts or conclusions from existing knowledge using logical rules.
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---
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## Knowledge Graph Components
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### **Node**
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A vertex in a graph representing an entity or concept.
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### **Edge**
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A connection between two nodes representing a relationship.
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### **Property**
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An attribute or characteristic of an entity or relationship (e.g., name, date, confidence score).
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### **Triplet**
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A basic unit of knowledge in RDF, consisting of a subject, predicate, and object (e.g., `<Apple_Inc> <founded_by> <Steve_Jobs>`).
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### **Temporal Graph**
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A knowledge graph that tracks changes over time, allowing queries about the state of the graph at specific time points.
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---
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## Entity Recognition & Extraction
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### **Named Entity Recognition (NER)**
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The process of identifying and classifying named entities in text into predefined categories such as persons, organizations, locations, dates, and more.
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### **Relationship Extraction**
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The task of identifying and extracting semantic relationships between entities in text.
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### **Entity Resolution**
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The process of determining when two entity mentions refer to the same real-world entity, also known as entity linking or deduplication.
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### **Coreference Resolution**
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The task of determining when two or more expressions in text refer to the same entity (e.g., "Apple" and "the company" referring to Apple Inc.).
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### **Event Detection**
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The task of identifying and classifying events (e.g., acquisitions, partnerships, announcements) in text.
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---
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## Ontology & Schema
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### **Ontology**
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A formal specification of concepts, relationships, and constraints in a domain, typically expressed in OWL (Web Ontology Language).
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### **Class**
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In ontologies, a category or type of entity (e.g., `Person`, `Organization`, `Location`).
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### **Axiom**
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A statement or rule that is accepted as true without proof, used in ontologies to define logical constraints and relationships.
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### **OWL (Web Ontology Language)**
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A W3C standard language for defining and instantiating ontologies on the web.
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### **Property**
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In ontologies, a relationship or attribute that connects entities or describes their characteristics.
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## Data Storage & Retrieval
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### **Embedding**
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A dense vector representation of text, images, or other data that captures semantic meaning in a continuous vector space. Used for similarity search and semantic matching.
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### **Vector Store**
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A database optimized for storing and searching high-dimensional vectors, used for semantic similarity search.
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### **Triplet Store**
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A database designed specifically for storing and querying RDF triplets.
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### **Graph Database**
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A database designed specifically for storing and querying graph-structured data.
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### **Hybrid Search**
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A search strategy that combines multiple retrieval methods, typically vector search and keyword search, to improve accuracy.
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---
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## Graph Analytics
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### **Centrality**
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A measure of the importance or influence of a node in a graph. Common centrality metrics include PageRank, betweenness centrality, and closeness centrality.
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### **PageRank**
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An algorithm used to measure the importance of nodes in a graph based on the structure of incoming links.
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### **Community Detection**
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The process of identifying groups or clusters of densely connected nodes in a graph.
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### **Graph Analytics**
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The application of graph algorithms (e.g., centrality, community detection) to gain insights from the structure of a knowledge graph.
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---
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## Query Languages
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### **Cypher**
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A declarative query language for graph databases, particularly Neo4j.
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### **SPARQL**
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A query language for RDF data, similar to SQL for relational databases.
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### **RDF (Resource Description Framework)**
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A W3C standard for representing information about resources in the form of subject-predicate-object triplets.
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---
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## Data Quality
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### **Conflict Resolution**
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The process of handling contradictory information from multiple sources in a knowledge graph.
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### **Deduplication**
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The process of identifying and removing duplicate records or entities from a dataset.
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### **Data Provenance**
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Information about the origin, history, and lineage of data, including sources, timestamps, and transformations.
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---
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## Technical Terms
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### **API (Application Programming Interface)**
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A set of functions and protocols that allow different software applications to communicate with each other.
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### **OCR (Optical Character Recognition)**
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Technology that converts images of text (e.g., scanned documents, photos) into machine-readable text.
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### **Pipeline**
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A sequence of data processing steps that transform raw data into a desired output format.
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### **Vector**
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A mathematical representation of data as an array of numbers, used in embeddings to capture semantic meaning.
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### **Visualization**
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The graphical representation of data, such as knowledge graphs, embeddings, or analytics.
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### **Web Scraping**
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The automated process of extracting data from websites.
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---
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## Semantica-Specific Terms
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### **Semantic Layer**
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An abstraction layer that provides a unified, business-friendly view of data by adding context, relationships, and meaning to raw data.
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### **Semantic Network**
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A knowledge representation that uses a graph structure to represent concepts and their relationships.
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### **Change Management**
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The process of tracking and managing changes to knowledge graphs over time, including version control and audit trails.
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### **Provenance Tracking**
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W3C PROV-O compliant tracking of data lineage and source attribution.
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---
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## See Also
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- [Core Concepts](concepts.md) — deeper explanation of key ideas
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- [Getting Started](getting-started.md) — first steps
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- [Modules Guide](modules.md) — every module explained
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- [API Reference](reference/) — technical reference
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