11 to 20 of 22 Results
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... |
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... |
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... |
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... |
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... |
Feb 21, 2024 - Analytic Computing
Asma, Zubaria; Hernández, Daniel; Galárraga, Luis; Flouris, Giorgos; Fundulaki, Irini; Hose, Katja, 2024, "Code and benchmark for NPCS, a Native Provenance Computation for SPARQL", https://doi.org/10.18419/DARUS-3973, DaRUS, V1
Code for the implementation and benchmark of NPCS, a Native Provenance Computation for SPARQL. The code in this dataset includes the implementation of the NPCS system, which is a middleware for SPARQL endpoints that rewrites queries to queries that annotate answers with provenance polynomials (i.e., how-provenance data). The translation rules imple... |
Feb 16, 2024 - Analytic Computing
Seifer, Philipp; Hernández, Daniel; Lämmel, Ralf; Staab, Steffen, 2024, "Code for From Shapes to Shapes", https://doi.org/10.18419/DARUS-3977, DaRUS, V1
This dataset contains the implementation code for an algorithm to infer SHACL shapes that the graph returned by an SPARQL CONSTRUCT query must satisfy if the input satisfies a given set of SHACL shapes. This dataset also includes an evaluation for the algorithm. The algorithm implemented in this dataset is proposed in the paper From Shapes to Shape... |
Feb 13, 2024 - Analytic Computing
Hedeshy, Ramin; Menges, Raphael; Staab, Steffen, 2024, "CNVVE Dataset clean audio samples", https://doi.org/10.18419/DARUS-3898, DaRUS, V1
This CNVVE Dataset contains clean audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or “mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders. These audio samples have undergone a thorough cleaning proces... |
Feb 13, 2024 - Analytic Computing
Hedeshy, Ramin; Menges, Raphael; Staab, Steffen, 2024, "Code for Training and Testing CNVVE", https://doi.org/10.18419/DARUS-3896, DaRUS, V1
This dataset consists of files used for training and testing the CNVVE Dataset. This dataset consists of 950 audio samples encompassing six distinct classes of voice expressions. These expressions were collected from 42 generous individuals who donated their voice recordings for the study. By making the dataset publicly accessible, we hope to facil... |
Feb 13, 2024 - Analytic Computing
Hedeshy, Ramin; Menges, Raphael; Staab, Steffen, 2024, "Raw audio samples of the CNVVE dataset", https://doi.org/10.18419/DARUS-3897, DaRUS, V1
This CNVVE Dataset contains raw audio samples encompassing six distinct classes of voice expressions, namely “Uh-huh” or “mm-hmm”, “Uh-uh” or “mm-mm”, “Hush” or “Shh”, “Psst”, “Ahem”, and Continuous humming, e.g., “hmmm.” Audio samples of each class are found in the respective folders. The samples are recorded through a dedicated website for data c... |
