OpenKE is a knowledge graph embedding framework designed to transform structured knowledge graphs into low-dimensional vector representations. It functions as a library for representation learning and a toolset for converting entities and relations into numerical embeddings. The project includes a link prediction engine to evaluate the likelihood of relationships between entities and identify missing facts in large-scale graphs. It provides a dedicated preprocessing tool to map raw entity and relation strings into numerical identifiers for machine learning training. The framework's capabilit
Starspace is a vector embedding framework designed for training high-dimensional representations of text and images. It functions as a machine learning system for neural ranking, text classification, and knowledge graph embedding, mapping different object types into a shared numerical space to facilitate retrieval and prediction tasks. The system includes specialized tools for knowledge graph completion and link prediction by representing entities and their relationships within a multi-relational vector space. It further provides capabilities for semantic content recommendation and large-scal
FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It ut
DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
The main features of accenture/ampligraph are: Knowledge Graph Embeddings, Knowledge Graphs.
Open-source alternatives to accenture/ampligraph include: thunlp/openke — OpenKE is a knowledge graph embedding framework designed to transform structured knowledge graphs into low-dimensional… facebookresearch/starspace — Starspace is a vector embedding framework designed for training high-dimensional representations of text and images.… falkordb/falkordb — FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… dstlry/dstlr — scalable knowledge graph construction from unstructured text. facebookresearch/blink — Entity Linker solution.