# Glossary Reference of terms and concepts used throughout Semantica. !!! tip "Finding a term" Use Ctrl+F to search this page. --- ## Core Concepts ### **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. ### **Entity** 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. ### **Knowledge Graph (KG)** A structured representation of knowledge using entities (nodes) and relationships (edges). KGs enable reasoning, querying, and semantic analysis of data. ### **Relationship** A connection between two entities that describes how they relate to each other (e.g., "works_for", "located_in", "founded_by"). ### **Semantic** Relating to meaning in language or logic. Semantic understanding goes beyond keywords to comprehend context and intent. --- ## Data Processing ### **Ingestion** The process of loading data from various sources (files, databases, APIs, streams) into a system for processing. ### **Normalization** The process of standardizing data into a consistent format (e.g., converting dates to ISO format, standardizing entity names). ### **Parsing** Extracting structured information from unstructured or semi-structured documents like PDFs, Word documents, or web pages. ### **Chunking** Breaking down large documents into smaller, manageable pieces while preserving context and meaning. --- ## Artificial Intelligence ### **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. ### **RAG (Retrieval Augmented Generation)** A technique that enhances LLM responses by retrieving relevant information from a knowledge base before generating an answer. ### **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. ### **Inference** The process of deriving new facts or conclusions from existing knowledge using logical rules. --- ## Knowledge Graph Components ### **Node** A vertex in a graph representing an entity or concept. ### **Edge** A connection between two nodes representing a relationship. ### **Property** An attribute or characteristic of an entity or relationship (e.g., name, date, confidence score). ### **Triplet** A basic unit of knowledge in RDF, consisting of a subject, predicate, and object (e.g., ` `). ### **Temporal Graph** A knowledge graph that tracks changes over time, allowing queries about the state of the graph at specific time points. --- ## Entity Recognition & Extraction ### **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. ### **Relationship Extraction** The task of identifying and extracting semantic relationships between entities in text. ### **Entity Resolution** The process of determining when two entity mentions refer to the same real-world entity, also known as entity linking or deduplication. ### **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.). ### **Event Detection** The task of identifying and classifying events (e.g., acquisitions, partnerships, announcements) in text. --- ## Ontology & Schema ### **Ontology** A formal specification of concepts, relationships, and constraints in a domain, typically expressed in OWL (Web Ontology Language). ### **Class** In ontologies, a category or type of entity (e.g., `Person`, `Organization`, `Location`). ### **Axiom** A statement or rule that is accepted as true without proof, used in ontologies to define logical constraints and relationships. ### **OWL (Web Ontology Language)** A W3C standard language for defining and instantiating ontologies on the web. ### **Property** In ontologies, a relationship or attribute that connects entities or describes their characteristics. --- ## Data Storage & Retrieval ### **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. ### **Vector Store** A database optimized for storing and searching high-dimensional vectors, used for semantic similarity search. ### **Triplet Store** A database designed specifically for storing and querying RDF triplets. ### **Graph Database** A database designed specifically for storing and querying graph-structured data. ### **Hybrid Search** A search strategy that combines multiple retrieval methods, typically vector search and keyword search, to improve accuracy. --- ## Graph Analytics ### **Centrality** A measure of the importance or influence of a node in a graph. Common centrality metrics include PageRank, betweenness centrality, and closeness centrality. ### **PageRank** An algorithm used to measure the importance of nodes in a graph based on the structure of incoming links. ### **Community Detection** The process of identifying groups or clusters of densely connected nodes in a graph. ### **Graph Analytics** The application of graph algorithms (e.g., centrality, community detection) to gain insights from the structure of a knowledge graph. --- ## Query Languages ### **Cypher** A declarative query language for graph databases, particularly Neo4j. ### **SPARQL** A query language for RDF data, similar to SQL for relational databases. ### **RDF (Resource Description Framework)** A W3C standard for representing information about resources in the form of subject-predicate-object triplets. --- ## Data Quality ### **Conflict Resolution** The process of handling contradictory information from multiple sources in a knowledge graph. ### **Deduplication** The process of identifying and removing duplicate records or entities from a dataset. ### **Data Provenance** Information about the origin, history, and lineage of data, including sources, timestamps, and transformations. --- ## Technical Terms ### **API (Application Programming Interface)** A set of functions and protocols that allow different software applications to communicate with each other. ### **OCR (Optical Character Recognition)** Technology that converts images of text (e.g., scanned documents, photos) into machine-readable text. ### **Pipeline** A sequence of data processing steps that transform raw data into a desired output format. ### **Vector** A mathematical representation of data as an array of numbers, used in embeddings to capture semantic meaning. ### **Visualization** The graphical representation of data, such as knowledge graphs, embeddings, or analytics. ### **Web Scraping** The automated process of extracting data from websites. --- ## Semantica-Specific Terms ### **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. ### **Change Management** The process of tracking and managing changes to knowledge graphs over time, including version control and audit trails. ### **Provenance Tracking** W3C PROV-O compliant tracking of data lineage and source attribution. --- ## See Also - [Core Concepts](concepts.md) — deeper explanation of key ideas - [Getting Started](getting-started.md) — first steps - [Modules Guide](modules.md) — every module explained - [API Reference](reference/) — technical reference