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Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction
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LibKGE – A knowledge graph embedding library for reproducible research
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Abstract:
LibKGE (https://github.com/uma-pi1/kge) is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. The key goals of LibKGE are to enable reproducible research, to provide a framework for comprehensive experimental studies, and to facilitate analyzing the contributions of individual components of training methods, model architectures, and evaluation methods. LibKGE is highly configurable and every experiment can be fully reproduced with a single configuration file. Individual components are decoupled to the extent possible so that they can be mixed and matched with each other. Implementations in LibKGE aim to be as efficient as possible without leaving the scope of Python/Numpy/PyTorch. A comprehensive logging mechanism and tooling facilitates in-depth analysis. LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. A comparative study (Ruffinelli et al., 2020) showed that LibKGE reaches competitive to state-of-the-art performance for many models with a modest amount of automatic hyperparameter tuning.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/61522 https://doi.org/10.18653/v1/2020.emnlp-demos.22 https://madoc.bib.uni-mannheim.de/61522/ https://madoc.bib.uni-mannheim.de/61522/1/LibKGE%20%E2%80%93%20A%20knowledge%20graph%20embedding%20library%20for%20reproducible%20research.pdf
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On aligning OpenIE extractions with Knowledge Bases: A case study
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On evaluating embedding models for knowledge base completion
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A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval
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Learning distributional token representations from visual features
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Methods for open information extraction and sense disambiguation on natural language text ; Methoden der Offenen Informationsextraktion und Bedeutungsdisambiguierung in Texten
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CORE: Context-aware open relation extraction with factorization machines
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Senti-LSSVM: Sentiment-oriented multi-relation extraction with latent structural SVM
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Werdy: Recognition and disambiguation of verbs and verb phrases with syntactic and semantic pruning
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