opensemanticsearch/open-semantic-search

Features

• Semantic Search Engines - Provides a comprehensive enterprise search engine that uses semantic retrieval and knowledge graph linking to explore large document collections.
• Enterprise Search - Provides a centralized search platform to index, organize, and retrieve information from large, diverse document repositories.
• Semantic Search - Expands search queries using thesauri and linguistic heuristics to identify synonyms and related concepts.
• Enterprise Discovery Platforms - Ingests diverse file formats and metadata to enable full-text search, knowledge graph visualization, and collaborative document annotation.
• Text Analytics - Automates the extraction of structured insights from unstructured documents using natural language processing and optical character recognition.
• Full Text Search - Executes keyword-based full-text search across diverse document collections and file formats.
• Full-Text Inverted Indexes - Powers full-text retrieval by mapping document terms to their locations, enabling rapid keyword lookups and complex boolean queries.
• Faceted Navigation - Provides interactive faceted navigation to filter and refine large search result sets by metadata attributes.
• Automated Text Analysis - Applies natural language processing to extract entities, topics, and sentiment from unstructured documents to enrich the searchable index.
• Entity Linking - Connects extracted entities and metadata into a structured network to support semantic navigation and relationship discovery.
• Optical Character Recognition - Performs optical character recognition on images and scanned documents to convert graphical content into searchable text.
• Data Pipelines and ETL - Transforms raw unstructured documents into structured data through sequential stages of extraction, normalization, and semantic enrichment.
• Collaborative Document Annotations - Enables collaborative document annotation, allowing teams to tag, categorize, and add notes to shared content.
• Knowledge and Information Management - Organizes internal document collections through collaborative tagging, metadata management, and faceted navigation to improve information accessibility.
• Data Ingestion Sources - Collects information from local files, websites, feeds, and databases to consolidate disparate content into a single searchable index.
• Query Expansion - Enhances search precision by automatically augmenting user queries with synonyms and related concepts from controlled vocabularies.
• Faceted Search Engines - Calculates real-time counts of document attributes to provide interactive filtering and drill-down navigation across large datasets.
• Faceted Search Implementation - Provides a web-based interface that allows users to navigate large datasets through interactive filters, semantic query expansion, and relationship mapping.
• Advanced Query Types - Supports complex search syntax including boolean logic, wildcards, and fuzzy matching for precise information retrieval.
• Automated Indexing - Triggers indexing updates through file system monitoring or notifications to ensure search results reflect content changes in real time.
• Distributed Text Analytics - Extracts structured data, named entities, and semantic relationships from unstructured documents to uncover patterns and insights automatically.
• Background Task Runners - Coordinates parallel indexing and analysis tasks using a background task queue to maintain high system throughput.
• Data Trend Visualizations - Generates interactive charts and relationship graphs from search results to visualize patterns and entity connections.
• Distributed Task Queues - Coordinates parallel document processing and indexing workflows by distributing heavy analysis tasks across multiple background worker nodes.
• Filesystem Change Monitors - Monitors file systems and data sources in real time to trigger automated indexing updates when content changes.
• Search-Based Navigation Interfaces - Offers a web-based interface for full-text, faceted, and exploratory search across document repositories.
• Data Explorers - Navigates complex datasets using conceptual relationships and thesauri to find relevant information beyond simple keyword matching.

Star history