
Pykg2vec
 Referenced in 5 articles
[sw30609]
 Pykg2vec: A Python Library for Knowledge Graph Embedding. Pykg2vec is an opensource Python library ... entities and relations in knowledge graphs. Pykg2vec’s flexible and modular software architecture currently ... implements 16 stateoftheart knowledge graph embedding algorithms, and is designed to easily ... educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built...

TransG
 Referenced in 4 articles
[sw34443]
 TransG : A Generative Mixture Model for Knowledge Graph Embedding. Recently, knowledge graph embedding, which projects ... proposes a novel Gaussian mixture model for embedding, TransG. The new model can discover latent ... vectors for embedding a fact triple. To the best of our knowledge, this ... first generative model for knowledge graph embedding, which is able to deal with multiple relation...

BioKEEN
 Referenced in 3 articles
[sw34085]
 library for learning and evaluating biological knowledge graph embeddings. Knowledge graph embeddings (KGEs) have received ... predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem ... learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate...

DGLKE
 Referenced in 3 articles
[sw34088]
 Training Knowledge Graph Embeddings at Scale. Knowledge graphs (KGs) are data structures that store information ... machine learning tasks is to compute knowledge graph embeddings. DGLKE is a high performance ... scalable package for learning largescale knowledge graph embeddings. The package is implemented...

AmpliGraph
 Referenced in 5 articles
[sw30610]
 with missing statements. Generate standalone knowledge graph embeddings. Develop and evaluate a new relational...

RotatE
 Referenced in 3 articles
[sw37755]
 RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. We study the problem ... representations of entities and relations in knowledge graphs for predicting missing links. The success ... present a new approach for knowledge graph embedding called RotatE, which is able to model ... model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model...

PyKEEN
 Referenced in 3 articles
[sw34084]
 Evaluating Knowledge Graph Emebddings. Recently, knowledge graph embeddings (KGEs) received significant attention, and several software ... PyKEEN 1.0 enables users to compose knowledge graph embedding models (KGEMs) based on a wide...

LibKGE
 Referenced in 2 articles
[sw39398]
 LibKGE  A knowledge graph embedding library for reproducible research. LibKGE ( https://github.com/umapi1/kge ... training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction ... analysis. LibKGE provides implementations of common knowledge graph embedding models and training methods...

ProjE
 Referenced in 3 articles
[sw34444]
 ProjE: Embedding Projection for Knowledge Graph Completion. With the large volume of new information created ... knowledge graph completion methods have been developed using lowdimensional graph embeddings. Although researchers continue ... knowledge graph by learning joint embeddings of the knowledge graph’s entities and edges...

GraphVite
 Referenced in 2 articles
[sw34087]
 general graph embedding engine, dedicated to highspeed and largescale embedding learning in various ... evaluation pipelines for 3 applications: node embedding, knowledge graph embedding and graph & highdimensional data...

TransT
 Referenced in 2 articles
[sw34445]
 TransT: TypeBased Multiple Embedding Representations for Knowledge Graph Completion. Knowledge graph completion with representation ... relation triples from the existing knowledge graphs by embedding entities and relations into a vector ... neglect semantic information contained in most knowledge graphs and the prior knowledge indicated ... based prior distributions, our approach generates multiple embedding representations of each entity in different contexts...

TorchKGE
 Referenced in 1 article
[sw41384]
 TorchKGE: Knowledge Graph embedding in Python and Pytorch. TorchKGE is a Python module for knowledge...

OpenKE
 Referenced in 5 articles
[sw30611]
 embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs ... support quick model validation and largescale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity ... toolkit, the embeddings of some existing largescale knowledge graphs pretrained by OpenKE ... answering. The toolkit, documentation, and pretrained embeddings are all released on http://openke.thunlp.org...

NeuralKG
 Referenced in 1 article
[sw41382]
 implements three different series of Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNNbased...

ConfE
 Referenced in 0 articles
[sw40559]
 Confidenceaware embedding for knowledge graph entity typing. Knowledge graphs (KGs) entity typing aims ... that is, (entity, entity type = ?). Recently, several embedding models are proposed for KG entity types...

ConceptNet Numberbatch
 Referenced in 1 article
[sw36069]
 semantic vectors (also known as word embeddings) than can be used directly as a representation ... open data project. ConceptNet is a knowledge graph that provides lots of ways to compute ... embeddings, while ConceptNet Numberbatch is a snapshot of just the word embeddings. These embeddings benefit ... that they have semistructured, common sense knowledge from ConceptNet, giving them...

KGAT
 Referenced in 2 articles
[sw32568]
 propose a new method named Knowledge Graph Attention Network (KGAT) which explicitly models the high ... fashion. It recursively propagates the embeddings from a node’s neighbors (which can be users...

autoBOT
 Referenced in 1 article
[sw40696]
 based, knowledge graphbased and relational features) and two types of document embeddings (nonsparse...

BoxE
 Referenced in 1 article
[sw41383]
 rules. Here, we propose a spatiotranslational embedding model, called BoxE, that simultaneously addresses ... performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show...

SWordInstaller
 Referenced in 1 article
[sw24389]
 installed (or without any knowledge of R). The functionality of embedding the scripts is somewhat ... data frame) or figures (plots, graphs...