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6.5 KiB
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.,
<Apple_Inc> <founded_by> <Steve_Jobs>). - 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 - Deep dive into fundamental concepts
- Getting Started - Begin your journey with Semantica
- API Reference - Technical documentation