285 machine learning datasets
285 dataset results
1.0 Version of OpenEA benchmark datasets. Please use the updated 2.0 version, that has been subsequently released.
The ZS-F-VQA dataset is a new split of the F-VQA dataset for zero-shot problem. Firstly we obtain the original train/test split of F-VQA dataset and combine them together to filter out the triples whose answers appear in top-500 according to its occurrence frequency. Next, we randomly divide this set of answers into new training split (a.k.a. seen) $\mathcal{A}_s$ and testing split (a.k.a. unseen) $\mathcal{A}_u$ at the ratio of 1:1. With reference to F-VQA standard dataset, the division process is repeated 5 times. For each $(i,q,a)$ triplet in original F-VQA dataset, it is divided into training set if $a \in \mathcal{A}_s$. Else it is divided into testing set. The overlap of answer instance between training and testing set in F-VQA are $2565$ compared to $0$ in ZS-F-VQA.
Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evaluation on the adversarial robustness of graph machine learning models. GRB has elaborated datasets, unified evaluation pipeline, modular coding framework, and reproducible leaderboards, which facilitate the developments of graph adversarial learning, summarizing existing progress and generating insights into future research.
We present a further analysis of visual modality incompleteness, benchmarking latest MMEA models on our proposed dataset MMEA-UMVM.
SemOpenAlex is an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. * SemOpenAlex is licensed under CC0, providing free and open access to the data. * We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources (ISNI, DOI, ORCID, ROR, Scopus, DOAJ, Wikidata, * Moreover, we provide embeddings for knowledge graph entities using high-performance computing.
SSN (short for Semantic Scholar Network) is a scientific papers summarization dataset which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph.
The Vent dataset is a large annotated dataset of text, emotions, and social connections. It comprises more than 33 millions of posts by nearly a million of users together with their social connections. Each post has an associated emotion. There are 705 different emotions, organized in 63 "emotion categories", forming a two-level taxonomy of affects.
This is a benchmark set for Traveling salesman problem (TSP) with characteristics that are different from the existing benchmark sets. In particular, it focuses on small instances which prove to be challenging for one or more state-of-the-art TSP algorithms. These instances are based on difficult instances of Hamiltonian cycle problem (HCP). This includes instances from literature, specially modified randomly generated instances, and instances arising from the conversion of other difficult problems to HCP.
This is a catalogue and repository of network datasets with the aim of aiding scientific research.
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 .
MuMiN is a misinformation graph dataset containing rich social media data (tweets, replies, users, images, articles, hashtags), spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade.
The MarKG dataset has 11,292 entities, 192 relations and 76,424 images, including 2,063 analogy entities and 27 analogy relations. The original intention of MarKG is to provide prior knowledge of analogy entities and relations for better multimodal analogical reasoning.
RARE consists of English AMR pairs with similarity scores that reflect the structural differences between them.
The ACM dataset contains papers published in KDD, SIGMOD, SIGCOMM, MobiCOMM, and VLDB and are divided into three classes (Database, Wireless Communication, Data Mining). An heterogeneous graph is constructed, which comprises 3025 papers, 5835 authors, and 56 subjects. Paper features correspond to elements of a bag-of-words represented of keywords.
Amazon Fine Foods is a dataset that consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plaintext review.
The IS-A dataset is a dataset of relations extracted from a medical ontology. The different entities in the ontology are related by the “is a” relation. For example, ‘acute leukemia’ is a ‘leukemia’. The dataset has 294,693 nodes with 356,541 edges between them.
Chickenpox Cases in Hungary is a spatio-temporal dataset of weekly chickenpox (childhood disease) cases from Hungary. It can be used as a longitudinal dataset for benchmarking the predictive performance of spatiotemporal graph neural network architectures. The dataset consists of a county-level adjacency matrix and time series of the county-level reported cases between 2005 and 2015. There are 2 specific related tasks:
WikiGraphs is a dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning. Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data.
InferWiki is a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sample is predictable with supportive data in the training set. Second, InferWiki initiates the evaluation following the open-world assumption and improves the inferential difficulty of the closed-world assumption, by providing manually annotated negative and unknown triples. Third, the dataset includes various inference patterns (e.g., reasoning path length and types) for comprehensive evaluation.
DPB-5L is a Multilingual KG dataset containing 5 KGs in English, French, Japanese, Greek, and Spanish. The dataset is used for the Knowledge Graph Completion and Entity Alignment task. DPB-5L (Greek) is a subset of DPB-5L with Greek KG.