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KaifAhmad1
409f3fdd27
- Added grid‑card layouts, icons, and admonitions to all reference pages - Removed all mkdocstrings ::: directives that caused alias‑resolution errors - Fixed export module alias issue (RDFExporter) by cleaning the docs - Updated README‑style sections for better readability - Added a PowerShell helper script (remove_mkdocstrings.ps1) for future clean‑ups
214 lines
6.5 KiB
Markdown
214 lines
6.5 KiB
Markdown
# Glossary
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A comprehensive reference of terms and concepts used in Semantica.
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---
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## A
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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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**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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**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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---
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## C
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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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**Class**
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: In ontologies, a category or type of entity (e.g., `Person`, `Organization`, `Location`).
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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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**Conflict Resolution**
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: The process of handling contradictory information from multiple sources in a knowledge graph.
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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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**Cypher**
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: A declarative query language for graph databases, particularly Neo4j.
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---
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## E
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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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**Entity**
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: A distinct object or concept in the real world, such as a person, place, organization, or event.
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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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**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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## G
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**Graph**
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: A data structure consisting of nodes (vertices) and edges (relationships) connecting them.
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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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## H
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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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## I
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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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**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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---
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## K
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**Knowledge Graph (KG)**
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: A structured representation of entities and their relationships, typically stored as a graph with nodes representing entities and edges representing relationships.
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**Knowledge Graph Quality Assurance (KG QA)**
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: The process of ensuring knowledge graph quality through completeness validation, consistency checking, and conflict detection.
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## L
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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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---
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## N
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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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**Node**
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: A vertex in a graph representing an entity or concept.
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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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---
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## O
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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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**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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**OWL (Web Ontology Language)**
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: A W3C standard language for defining and instantiating ontologies on the web.
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## P
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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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**Pipeline**
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: A sequence of data processing steps that transform raw data into a desired output format.
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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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**Provenance**
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: Information about the origin, history, and lineage of data, including sources, timestamps, and transformations.
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## R
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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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**RDF (Resource Description Framework)**
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: A W3C standard for representing information about resources in the form of subject-predicate-object triples.
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**Reasoning**
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: The process of deriving new knowledge from existing facts using logical rules and inference.
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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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## S
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**Semantic**
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: Relating to meaning in language or logic.
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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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**SPARQL**
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: A query language for RDF data, similar to SQL for relational databases.
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## T
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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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**Triple**
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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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**Triple Store**
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: A database designed specifically for storing and querying RDF triples.
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## V
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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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**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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**Visualization**
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: The graphical representation of data, such as knowledge graphs, embeddings, or analytics.
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## W
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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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## See Also
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- [Core Concepts](concepts.md) - Deep dive into fundamental concepts
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- [Getting Started](getting-started.md) - Begin your journey with Semantica
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- [API Reference](reference/core.md) - Technical documentation
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