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Metadata Source: DaRUS

371 to 380 of 2,674 Results

Supplementary material for 'Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment' Jul 8, 2024 - Institute of Thermodynamics and Thermal Process Engineering Grunenberg, Lars; Kessler, Christopher; Teh, Tiong Wei; Schuldt, Robin; Heck, Fabian; Kästner, Johannes; Gross, Joachim; Hansen, Niels; Lotsch, Bettina V., 2024, "Supplementary material for 'Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment'", https://doi.org/10.18419/DARUS-3269, DaRUS, V1, UNF:6:vHsUiLwzEUUMiOsYM8xu+Q== [fileUNF] This dataset contains input files and results from Grand Canonical Monte Carlo (GCMC) adsorption simulation and Molecular Dynamics (MD) Simulation. All data is presented in a jupyter notebook and for a fast overview without executing the notebook also as PDF-file. Furthermore the dataset contains the modified cif files of COF PI-3, including partia...
Dissertation Cicac-Hudi(Universität Stuttgart) Jul 6, 2024IAC - Gudat group Supplement of Mario Cicac-Hudi's PhD Thesis
IAC - Gudat group(Universität Stuttgart) Jul 6, 2024Institute of Inorganic Chemistry
Code for Faithful Embeddings for EL++ Knowledge Bases Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Potyka, Nico; Tran, Trung-Kien; Nayyeri, Mojtaba; Staab, Steffen, 2024, "Code for Faithful Embeddings for EL++ Knowledge Bases", https://doi.org/10.18419/DARUS-3989, DaRUS, V1 This is the official pytorch implementation of the paper "Faithful embeddings for EL++ Knowledge Bases" published in ISWC 2022. The code was implemented based on el-embeddings. The code can be used to reproduce the experiments on subsumption reasoning. To execute the code, follow the instructions in the README.md file. For more info, please check t...
Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Nayyeri, Mojtaba; Pan, Shirui; Staab, Steffen, 2024, "Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs", https://doi.org/10.18419/DARUS-3979, DaRUS, V1 This is a Pytorch implementation of the paper Shrinking Embeddings for Hyper-relational Knowledge Graphs published in ACL'23. This code is used to reproduce the experiments of the method ShrinkE, a geometric embedding approach for hyper-relational knowledge graphs. The code is implemented with Python 3 and pytorch. The code is tested on public data...
Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Nayyeri, Mojtaba; Cochez, Michael; Staab, Steffen, 2024, "Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction", https://doi.org/10.18419/DARUS-3988, DaRUS, V1 This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For mor...
Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24) Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Nayyeri, Mojtaba; Luo, Linhao; Wang, Zihao; Pan, Shirui; Staab, Steffen, 2024, "Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)", https://doi.org/10.18419/DARUS-3978, DaRUS, V1 This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)". NestE is a knowledge graph embedding method that can encode nested facts represented by quoted triples (h,r,t) in which the subject and object are triples themselves, e.g., ((BarackObama, holds_position, Preside...
Code for Ultrahyperbolic Knowledge Graph Embeddings Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Zhu, Shichao; Nayyeri, Mojtaba; Xu, Chengjin; Pan, Shirui; Staab, Steffen, 2024, "Code for Ultrahyperbolic Knowledge Graph Embeddings", https://doi.org/10.18419/DARUS-4342, DaRUS, V1 This is a Pytorch implementation of the paper Ultrahyperbolic Knowledge Graph Embeddings published in KDD 2022. This code is used to reproduce the experiments of the method UltraE, a geometric embedding approach for knowledge graph embeddings. The code is tested on public datasets which can be downloaded from KGEmb. To execute the code, follow the...
Code for Pseudo-Riemannian Graph Convolutional Networks Jul 5, 2024 - Analytic Computing Xiong, Bo; Zhu, Shichao; Potyka, Nico; Pan, Shirui; Zhou, Chuan; Staab, Steffen, 2024, "Code for Pseudo-Riemannian Graph Convolutional Networks", https://doi.org/10.18419/DARUS-4340, DaRUS, V1, UNF:6:XC5GdaJdFoY7V7SNqvdoiQ== [fileUNF] This dataset is the official implementation of Pseudo-Riemannian Graph Convolutional Networks in PyTorch, based on HGCN implementation. This code is used to reproduce the experiments of the paper. Datasets are provided in the /data directly. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or...
KnowGraphs (EU)(Universität Stuttgart) Jul 3, 2024Analytic Computing Project Website: https://www.ki.uni-stuttgart.de/departments/ac/research/projects/knowngraphs/

Supplementary material for 'Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment'

Jul 8, 2024 - Institute of Thermodynamics and Thermal Process Engineering

Grunenberg, Lars; Kessler, Christopher; Teh, Tiong Wei; Schuldt, Robin; Heck, Fabian; Kästner, Johannes; Gross, Joachim; Hansen, Niels; Lotsch, Bettina V., 2024, "Supplementary material for 'Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment'", https://doi.org/10.18419/DARUS-3269, DaRUS, V1, UNF:6:vHsUiLwzEUUMiOsYM8xu+Q== [fileUNF]

This dataset contains input files and results from Grand Canonical Monte Carlo (GCMC) adsorption simulation and Molecular Dynamics (MD) Simulation. All data is presented in a jupyter notebook and for a fast overview without executing the notebook also as PDF-file. Furthermore the dataset contains the modified cif files of COF PI-3, including partia...

Dissertation Cicac-Hudi(Universität Stuttgart)

Jul 6, 2024IAC - Gudat group

Supplement of Mario Cicac-Hudi's PhD Thesis

IAC - Gudat group(Universität Stuttgart)

Jul 6, 2024Institute of Inorganic Chemistry

Code for Faithful Embeddings for EL++ Knowledge Bases

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Potyka, Nico; Tran, Trung-Kien; Nayyeri, Mojtaba; Staab, Steffen, 2024, "Code for Faithful Embeddings for EL++ Knowledge Bases", https://doi.org/10.18419/DARUS-3989, DaRUS, V1

This is the official pytorch implementation of the paper "Faithful embeddings for EL++ Knowledge Bases" published in ISWC 2022. The code was implemented based on el-embeddings. The code can be used to reproduce the experiments on subsumption reasoning. To execute the code, follow the instructions in the README.md file. For more info, please check t...

Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Nayyeri, Mojtaba; Pan, Shirui; Staab, Steffen, 2024, "Code for Shrinking Embeddings for Hyper-relational Knowledge Graphs", https://doi.org/10.18419/DARUS-3979, DaRUS, V1

This is a Pytorch implementation of the paper Shrinking Embeddings for Hyper-relational Knowledge Graphs published in ACL'23. This code is used to reproduce the experiments of the method ShrinkE, a geometric embedding approach for hyper-relational knowledge graphs. The code is implemented with Python 3 and pytorch. The code is tested on public data...

Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Nayyeri, Mojtaba; Cochez, Michael; Staab, Steffen, 2024, "Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction", https://doi.org/10.18419/DARUS-3988, DaRUS, V1

This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For mor...

Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Nayyeri, Mojtaba; Luo, Linhao; Wang, Zihao; Pan, Shirui; Staab, Steffen, 2024, "Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)", https://doi.org/10.18419/DARUS-3978, DaRUS, V1

This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)". NestE is a knowledge graph embedding method that can encode nested facts represented by quoted triples (h,r,t) in which the subject and object are triples themselves, e.g., ((BarackObama, holds_position, Preside...

Code for Ultrahyperbolic Knowledge Graph Embeddings

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Zhu, Shichao; Nayyeri, Mojtaba; Xu, Chengjin; Pan, Shirui; Staab, Steffen, 2024, "Code for Ultrahyperbolic Knowledge Graph Embeddings", https://doi.org/10.18419/DARUS-4342, DaRUS, V1

This is a Pytorch implementation of the paper Ultrahyperbolic Knowledge Graph Embeddings published in KDD 2022. This code is used to reproduce the experiments of the method UltraE, a geometric embedding approach for knowledge graph embeddings. The code is tested on public datasets which can be downloaded from KGEmb. To execute the code, follow the...

Code for Pseudo-Riemannian Graph Convolutional Networks

Jul 5, 2024 - Analytic Computing

Xiong, Bo; Zhu, Shichao; Potyka, Nico; Pan, Shirui; Zhou, Chuan; Staab, Steffen, 2024, "Code for Pseudo-Riemannian Graph Convolutional Networks", https://doi.org/10.18419/DARUS-4340, DaRUS, V1, UNF:6:XC5GdaJdFoY7V7SNqvdoiQ== [fileUNF]

This dataset is the official implementation of Pseudo-Riemannian Graph Convolutional Networks in PyTorch, based on HGCN implementation. This code is used to reproduce the experiments of the paper. Datasets are provided in the /data directly. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or...

KnowGraphs (EU)(Universität Stuttgart)

Jul 3, 2024Analytic Computing

Project Website: https://www.ki.uni-stuttgart.de/departments/ac/research/projects/knowngraphs/

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