3 Repos
Assembling large-scale datasets of interconnected entities into graph structures for AI.
Distinct from Large-Scale Dataset Management: Focuses on the assembly and construction of the graph, not just the storage management of the resulting dataset.
Explore 3 awesome GitHub repositories matching data & databases · Knowledge Graph Construction. Refine with filters or upvote what's useful.
KnowledgeGraphData is a collection of structured datasets and corpora designed to provide a foundational layer for cognitive intelligence and artificial intelligence systems. It primarily consists of large-scale Chinese knowledge graph datasets, including entity-relation data and NLP training sets used to drive semantic understanding and automated question answering. The project focuses on the construction and export of massive entity-attribute-value graphs, organizing knowledge into portable formats. It provides specialized domain partitioning to tailor information retrieval for professional
Assembles massive datasets of interconnected entities to create a foundational layer for cognitive artificial intelligence.
This library is a PHP tool for programmatically defining, building, and exporting structured data graphs. It provides a fluent, object-oriented interface that allows developers to construct complex, nested metadata structures that comply with standardized vocabulary requirements for search engine indexing. The library distinguishes itself through its support for multi-typed entity modeling, which enables the combination of several classification categories into a single, unified entity. It also features a graph-based modeling system that facilitates the linking and cross-referencing of relate
Links multiple related entities into a unified collection to represent complex relationships.
Entity-Relation-Extraction is a machine learning framework designed to identify entities and their logical connections within unstructured text. It functions as a pipeline that transforms raw documents into structured knowledge graphs by utilizing deep learning models and transformer architectures. The project distinguishes itself through a schema-driven approach, which maps extracted information to predefined relational templates to ensure output consistency. It employs a multi-stage process that combines sequence-labeling token classification with contextual encoding to delineate entity bou
Populates structured databases by automatically extracting relationships between entities from large volumes of text.