# Glossary

A comprehensive reference of terms and concepts used in Semantica.

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## A

**Agent**
: 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.

**API (Application Programming Interface)**
: A set of functions and protocols that allow different software applications to communicate with each other.

**Axiom**
: A statement or rule that is accepted as true without proof, used in ontologies to define logical constraints and relationships.

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## C

**Centrality**
: A measure of the importance or influence of a node in a graph. Common centrality metrics include PageRank, betweenness centrality, and closeness centrality.

**Class**
: In ontologies, a category or type of entity (e.g., `Person`, `Organization`, `Location`).

**Community Detection**
: The process of identifying groups or clusters of densely connected nodes in a graph.

**Conflict Resolution**
: The process of handling contradictory information from multiple sources in a knowledge graph.

**Coreference Resolution**
: 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.).

**Cypher**
: A declarative query language for graph databases, particularly Neo4j.

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## E

**Embedding**
: 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.

**Entity**
: A distinct object or concept in the real world, such as a person, place, organization, or event.

**Entity Resolution**
: The process of determining when two entity mentions refer to the same real-world entity, also known as entity linking or deduplication.

**Event Detection**
: The task of identifying and classifying events (e.g., acquisitions, partnerships, announcements) in text.

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## G

**Graph**
: A data structure consisting of nodes (vertices) and edges (relationships) connecting them.

**GraphRAG (Graph-Augmented Retrieval Augmented Generation)**
: 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

**Hybrid Search**
: A search strategy that combines multiple retrieval methods, typically vector search and keyword search, to improve accuracy.

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## I

**Inference**
: The process of deriving new facts or conclusions from existing knowledge using logical rules.

**Ingestion**
: The process of loading data from various sources (files, databases, APIs, streams) into a system for processing.

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## K

**Knowledge Graph (KG)**
: A structured representation of entities and their relationships, typically stored as a graph with nodes representing entities and edges representing relationships.

**Knowledge Graph Quality Assurance (KG QA)**
: The process of ensuring knowledge graph quality through completeness validation, consistency checking, and conflict detection.

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## L

**LLM (Large Language Model)**
: 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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## N

**Named Entity Recognition (NER)**
: The process of identifying and classifying named entities in text into predefined categories such as persons, organizations, locations, dates, and more.

**Node**
: A vertex in a graph representing an entity or concept.

**Normalization**
: The process of standardizing data into a consistent format (e.g., converting dates to ISO format, standardizing entity names).

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## O

**OCR (Optical Character Recognition)**
: Technology that converts images of text (e.g., scanned documents, photos) into machine-readable text.

**Ontology**
: A formal specification of concepts, relationships, and constraints in a domain, typically expressed in OWL (Web Ontology Language).

**OWL (Web Ontology Language)**
: A W3C standard language for defining and instantiating ontologies on the web.

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## P

**PageRank**
: An algorithm used to measure the importance of nodes in a graph based on the structure of incoming links.

**Pipeline**
: A sequence of data processing steps that transform raw data into a desired output format.

**Property**
: In ontologies, a relationship or attribute that connects entities or describes their characteristics.

**Provenance**
: Information about the origin, history, and lineage of data, including sources, timestamps, and transformations.

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## R

**RAG (Retrieval Augmented Generation)**
: A technique that enhances LLM responses by retrieving relevant information from a knowledge base before generating an answer.

**RDF (Resource Description Framework)**
: A W3C standard for representing information about resources in the form of subject-predicate-object triples.

**Reasoning**
: The process of deriving new knowledge from existing facts using logical rules and inference.

**Relationship Extraction**
: The task of identifying and extracting semantic relationships between entities in text.

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## S

**Semantic**
: Relating to meaning in language or logic.

**Semantic Layer**
: An abstraction layer that provides a unified, business-friendly view of data by adding context, relationships, and meaning to raw data.

**Semantic Network**
: A knowledge representation that uses a graph structure to represent concepts and their relationships.

**SPARQL**
: A query language for RDF data, similar to SQL for relational databases.

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## T

**Temporal Graph**
: A knowledge graph that tracks changes over time, allowing queries about the state of the graph at specific time points.

**Triple**
: A basic unit of knowledge in RDF, consisting of a subject, predicate, and object (e.g., `<Apple_Inc> <founded_by> <Steve_Jobs>`).

**Triple Store**
: A database designed specifically for storing and querying RDF triples.

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## V

**Vector**
: A mathematical representation of data as an array of numbers, used in embeddings to capture semantic meaning.

**Vector Store**
: A database optimized for storing and searching high-dimensional vectors, used for semantic similarity search.

**Visualization**
: The graphical representation of data, such as knowledge graphs, embeddings, or analytics.

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## W

**Web Scraping**
: The automated process of extracting data from websites.

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## See Also

- [Core Concepts](concepts.md) - Deep dive into fundamental concepts
- [Getting Started](getting-started.md) - Begin your journey with Semantica
- [API Reference](reference/core.md) - Technical documentation
