# Glossary A comprehensive reference of terms and concepts used in Semantica. --- ## 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. --- ## 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. --- ## 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. --- ## 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. --- ## H **Hybrid Search** : A search strategy that combines multiple retrieval methods, typically vector search and keyword search, to improve accuracy. --- ## 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. --- ## 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. --- ## 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. --- ## 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). --- ## 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. --- ## 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. --- ## 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. --- ## 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. --- ## 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., ` `). **Triple Store** : A database designed specifically for storing and querying RDF triples. --- ## 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. --- ## W **Web Scraping** : The automated process of extracting data from websites. --- ## 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