OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification. MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification). WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction). PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).
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The KACC benchmark consists of three subtasks that can be applied to knowledge graphs: knowledge abstraction, knowledge concretization and knowledge completion. The knowledge abstraction subtask contains tasks of concept inference, schema prediction and concept graph completion on the two-view KG. The knowledge concretization subtask requires models to do entity graph completion based on the two subgraphs. The knowledge completion subtask consists of typical single-view knowledge graph completion tasks for each subgraph. KACC contains 999,902 entities in the entity graph, with 691 types of relations . The concept graph contains 21,293 concepts with 198 types of meta-relations. There are 2,367,971 cross-links between the two.
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MMKG is a collection of three knowledge graphs for link prediction and entity matching research. Contrary to other knowledge graph datasets, these knowledge graphs contain both numerical features and images for all entities as well as entity alignments between pairs of KGs. The three knowledge graphs augmented with numerical features and images are called FB15k, YAGO15k, and DBPEDIA15k.
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The WorldKG knowledge graph is a comprehensive large-scale geospatial knowledge graph based on OpenStreetMap that provides a semantic representation of geographic entities from over 188 countries. WorldKG contains a higher number of representations of geographic entities compared to other knowledge graphs and can be used as an underlying data source for various applications such as geospatial question
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ENT-DESC involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of
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KG20C is a Knowledge Graph about high quality papers from 20 top computer science Conferences. It can serve as a standard benchmark dataset in scholarly data analysis for several tasks, including knowledge graph embedding, link prediction, recommendation systems, and question answering .
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The dataset is constructed from an Amazon review corpus by integrating both user-agent dialogue and custom knowledge graphs for recommendation.
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DTBM is a benchmark dataset for Digital Twins that reflects these characteristics and look into the scaling challenges of different knowledge graph technologies.
A new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia.
NLPContributionGraph was introduced as Task 11 at SemEval 2021 for the first time. The task is defined on a dataset of Natural Language Processing (NLP) scholarly articles with their contributions structured to be integrable within Knowledge Graph infrastructures such as the Open Research Knowledge Graph.
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Contains 1000 semantic queries and the corresponding English, German and Portuguese verbalizations for EventKG - an event-centric knowledge graph with more than 970 thousand events.
The FB1.5M dataset is a benchmark for Knowledge Graph Completion. It is based on Freebase and it contains 30 relations with less than 500 triplets as low-resource relations.
…It consists of entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types.
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…Knowledge graphs (KG) provide a visual representation in a graph that can reason and interpret from the underlying data, making them suitable for use in education and interactive learning. Creating knowledge graphs from unstructured text is challenging without an ontology or annotated dataset. However, data annotation for cybersecurity needs domain experts. This dataset can be used to construct knowledge graphs to teach cybersecurity and promote cognitive learning.
ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges.
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…We introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge.
TextWorld KG is a dynamic Knowledge Graph (KG) extraction dataset. It is based on a set of text-based games generated using.