• Pykg2vec

  • Referenced in 5 articles [sw30609]
  • Pykg2vec: A Python Library for Knowledge Graph Embedding. Pykg2vec is an open-source Python library ... entities and relations in knowledge graphs. Pykg2vec’s flexible and modular software architecture currently ... implements 16 state-of-the-art 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...
  • DGL-KE

  • 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. DGL-KE is a high performance ... scalable package for learning large-scale knowledge graph embeddings. The package is implemented...
  • AmpliGraph

  • Referenced in 5 articles [sw30610]
  • with missing statements. Generate stand-alone 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/uma-pi1/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 low-dimensional 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 high-speed and large-scale embedding learning in various ... evaluation pipelines for 3 applications: node embedding, knowledge graph embedding and graph & high-dimensional data...
  • TransT

  • Referenced in 2 articles [sw34445]
  • TransT: Type-Based 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 large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity ... toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE ... answering. The toolkit, documentation, and pre-trained 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, GNN-based...
  • ConfE

  • Referenced in 0 articles [sw40559]
  • Confidence-aware 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 semi-structured, 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 graph-based and relational features) and two types of document embeddings (non-sparse...
  • BoxE

  • Referenced in 1 article [sw41383]
  • rules. Here, we propose a spatio-translational 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...