[email protected]'21) | PythonRepo" /> [email protected]'21) | PythonRepo">

CaLiGraph Ontology as a Challenge for Semantic Reasoners ([email protected]'21)

Overview

CaLiGraph for Semantic Reasoning Evaluation Challenge

This repository contains code and data to use CaLiGraph as a benchmark dataset in the Semantic Reasoning Evaluation Challange at the International Semantic Web Conference 2021 (ISWC'21).

The paper describing the dataset characteristics and results for well-known reasoners will be linked here as soon as a preprint is available.

Datasets

We use CaLiGraph version 2.1.0 as foundation for the challenge dataset. In particular, we use the files caligraph-ontology.nt.bz2 and caligraph-instances_types.nt.bz2 to generate our sample data.

We provide sample datasets having roughly 10n classes with n in [1,6]. The datasets and all potentially inferrable assertions can be found here. Please refer to our paper if you are interested in how the sample datasets were constructed.

Evaluated Reasoners

We evaluated the following reasoners with our sample datasets:

To evaluate the reasoners, we used their connectors in OWL API. The source code for the evaluation of the reasoners can be found in the folder reasoner_evaluation. As Pellet needs an OWL API version lower than 4 (while the others need a version higher than 4) we provide two pom.xml files. Depending on which reasoner you want to run, you have to use the correct one. Further, you have to uncomment the respective reasoners in the getReasoners() function of the java file org.unima.nheist.App.

Alternatively, you can use the two provided jar files to run the reasoners with the datasets. First download the sample dataset you want to run the reasoners on, then run the jar file and provide the location of the dataset as first argument like this:

java -jar semrec-caligraph-elk-hermit.jar <PATH-TO-DATASET-FILE> &> log_elk-hermit.txt

The result is a realization of the input dataset through the selected reasoners. Have a look at the log file (in the case of the example: log_elk-hermit.txt) for additional information about the reasoning process.

The precomputed results for all the sample datasets can be found here.

Owner
Nico Heist
Scientific Researcher and PhD Candidate at Data and Web Science Group, University of Mannheim
Nico Heist
Generating Band-Limited Adversarial Surfaces Using Neural Networks

Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv

3 Jul 26, 2022
Wenet STT Python

Wenet STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using WeNet models for sp

David Zurow 33 Feb 21, 2022
Contrastive Learning for Metagenomic Binning

CLMB A simple framework for CLMB - a novel deep Contrastive Learningfor Metagenomic Binning Created by Pengfei Zhang, senior of Department of Computer

1 Sep 14, 2022
AI drive app that can help user become beautiful.

爱美丽 Beauty 简体中文 Features Beauty is an AI drive app that can help user become beautiful. it contain those functions: face score cheek face beauty repor

Starved Midnight 1 Jan 30, 2022
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

QAConv Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting This PyTorch code is proposed in

Shengcai Liao 166 Dec 28, 2022
Noether Networks: meta-learning useful conserved quantities

Noether Networks: meta-learning useful conserved quantities This repository contains the code necessary to reproduce experiments from "Noether Network

Dylan Doblar 33 Nov 23, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022
Implementation of U-Net and SegNet for building segmentation

Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te

Martin.w-e 3 Dec 07, 2022
LightLog is an open source deep learning based lightweight log analysis tool for log anomaly detection.

LightLog Introduction LightLog is an open source deep learning based lightweight log analysis tool for log anomaly detection. Function description [BG

25 Dec 17, 2022
This is an official pytorch implementation of Fast Fourier Convolution.

Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma

pkumi 199 Jan 03, 2023
Arch-Net: Model Distillation for Architecture Agnostic Model Deployment

Arch-Net: Model Distillation for Architecture Agnostic Model Deployment The official implementation of Arch-Net: Model Distillation for Architecture A

MEGVII Research 22 Jan 05, 2023
OpenL3: Open-source deep audio and image embeddings

OpenL3 OpenL3 is an open-source Python library for computing deep audio and image embeddings. Please refer to the documentation for detailed instructi

Music and Audio Research Laboratory - NYU 326 Jan 02, 2023
OoD Minimum Anomaly Score GAN - Code for the Paper 'OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary'

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary Out-of-Distribution Minimum Anomaly Score GAN (OMASGAN) C

- 8 Sep 27, 2022
Project to create an open-source 6 DoF input device

6DInputs A Project to create open-source 3D printed 6 DoF input devices Note the plural ('6DInputs' and 'devices') in the headings. We would like seve

RepRap Ltd 47 Jul 28, 2022
Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

This repository contains code for the paper Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiati

8 Aug 28, 2022
Array Camera Ptychography

Array Camera Ptychography This repository provides the code for the following papers: Schulz, Timothy J., David J. Brady, and Chengyu Wang. "Photon-li

Brady lab in Optical Sciences 1 Nov 15, 2021
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022
2.86% and 15.85% on CIFAR-10 and CIFAR-100

Shake-Shake regularization This repository contains the code for the paper Shake-Shake regularization. This arxiv paper is an extension of Shake-Shake

Xavier Gastaldi 294 Nov 22, 2022
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark We propose a benchmark to evaluate different quantization algorithms on vari

494 Dec 29, 2022
Video Corpus Moment Retrieval with Contrastive Learning (SIGIR 2021)

Video Corpus Moment Retrieval with Contrastive Learning PyTorch implementation for the paper "Video Corpus Moment Retrieval with Contrastive Learning"

ZHANG HAO 42 Dec 29, 2022