Publication | Open Access
OpenKE: An Open Toolkit for Knowledge Embedding
342
Citations
21
References
2018
Year
Unknown Venue
Open ToolkitEngineeringMachine LearningKnowledge ExtractionSemantic WebText MiningWord EmbeddingsNatural Language ProcessingKnowledge EmbeddingRepresentation LearningKnowledge Graph EmbeddingsInformation RetrievalData ScienceLarge-scale Knowledge RepresentationEmbeddingsKnowledge RepresentationKnowledge DiscoveryComputer ScienceKnowledge GraphsKnowledge BaseSemantic Graph
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp.org/.
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