Dataset introduced by Xifeng Yan et al.
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Multi-Modal Hate Speech Detection with Graph Context.
The dataset contains constructed multi-modal features (visual and textual), pseudo-labels (on heritage values and attributes), and graph structures (with temporal, social, and spatial links) constructed using User-Generated Content data collected from Flickr social media platform in three global cities containing UNESCO World Heritage property (Amsterdam, Suzhou, Venice). The motivation of data collection in this project is to provide datasets that could be both directly applicable for ML communities as test-bed, and theoretically informative for heritage and urban scholars to draw conclusions on for planning decision-making.
A large dataset from the Inductive Link Prediction Challenge 2022. Training graph contains 46K entities, 130 relations, 202K triples. Inference graph contains 30K entities, 130 relations, 77K triples. Validation and test triples to predict belong to the inference graph.
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A small dataset from the Inductive Link Prediction Challenge 2022. Training graph contains 10K entities, 96 relations, 78K triples. Inference graph contains 7K entities, 96 relations, 21K triples. Validation and test triples to predict belong to the inference graph.
IMCPT-SparseGM dataset is a new visual graph matching benchmark addressing partial matching and graphs with larger sizes, based on the novel stereo benchmark Image Matching Challenge PhotoTourism (IMC-PT) 2020. This dataset is released in CVPR 2023 paper Deep Learning of Partial Graph Matching via Differentiable Top-K.
KGRC-RDF-star is an RDF-star dataset converted from KGRC-RDF, which is a Knowledge graph dataset of novel stories.
The PART-OF dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are parts of the human body. The dataset has 16,894 nodes with 19,436 edges between them.
ReviewRobot Dataset Overview This repository contains data for paper ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis. [Dataset]
The Room environment - v0 There is a newer version, v1
The Room environment - v1 For the documentation of RoomEnv-v0, click the corresponding buttons.
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Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graphlike in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problem using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of fact
TextWorld KG is a dynamic Knowledge Graph (KG) extraction dataset. It is based on a set of text-based games generated using. That framework allows to extract the underlying partial KG for every state, i.e., the subgraph that represents the agent’s partial knowledge of the world – what it has observed so far. All games share the same overarching theme: the agent finds itself hungry in a simple modern house with the goal of gathering ingredients and cooking a meal.
ZeroKBC is comprehensive benchmark that covers all scenarios of zero-shot Knowledge Base Completion (KBC) task. It has 3 zero-shot scenarios with 8 fine-grained settings.
This file contains the data and code for the publication "The Federal Reserve's Response to the Global Financial Crisis and Its Long-Term Impact: An Interrupted Time-Series Natural Experimental Analysis" by A. C. Kamkoum, 2023.
This is the list of all doges of the Venetian Republic, as well as their wives, if there's a record that they existed. They include name, surname if known, and date of their office, as well as the date of their weddings. Data has been extracted from the Wikipedia, with some errors fixed checking against other sources.
This dataset is composed of paired videos of people dancing 3 different music styles: Ballet, Michael Jackson and Salsa. It contains multimodal data (visual data, temporal-graphs and audio) careful-selected from publicly available videos of dancers performing representative movements of the music style and audio data from the respective styles.
A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality -- allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluidic intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abs
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