BioGRID is a biomedical interaction repository with data compiled through comprehensive curation efforts. The current index is version 4.2.192 and searches 75,868 publications for 1,997,840 protein and genetic interactions, 29,093 chemical interactions and 959,750 post translational modifications from major model organism species. Source: https://thebiogrid.org/
A social network of LastFM users which was collected from the public API in March 2020. Nodes are LastFM users from Asian countries and edges are mutual follower relationships between them. The vertex features are extracted based on the artists liked by the users. The task related to the graph is multinomial node classification - one has to predict the location of users. This target feature was derived from the country field for each user.
An expert-annotated word similarity dataset which provides a highly reliable, yet challenging, benchmark for rare word representation techniques. Source: Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models
Consists of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Source: ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
In MutualFriends, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend. Source: Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
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 . For more information and download, please see the dataset homepage.
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. Source: https://arxiv.org/pdf/1906.05939.pdf
OLPBENCH is a large Open Link Prediction benchmark, which was derived from the state-of-the-art Open Information Extraction corpus OPIEC (Gashteovski et al., 2019). OLPBENCH contains 30M open triples, 1M distinct open relations and 2.5M distinct mentions of approximately 800K entities. Open Link Prediction is defined as follows: Given an Open Knowledge Graph and a question consisting of an entity mention and an open relation, predict mentions as answers. A predicted mention is correct if it is a mention of the correct answer entity. For example, given the question (“NBC-TV”, “has office in”, ?), correct answers include “NYC” and “New York”. Source: OLPBENCH
ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs.
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.
KGRC-RDF-star is an RDF-star dataset converted from KGRC-RDF, which is a Knowledge graph dataset of novel stories. RDF-star (also known as RDF*) introduces quoted triple (QT), which is a triple used as the subject or object of another triple. KGRC-RDF-star is a complex RDF-star graph dataset that contains nested structures of statements and scenes, e.g., "Person A said "Person B saw "Person C was in D" " ." The same s, p, and o combinations may occur in different scenes when the KGRC-RDF is converted to the KGRC-RDF-star. It is necessary to distinguish these QTs and assign different metadata to them. Therefore, we solved this problem by assigning a unique ID to each QT and nested these triples as a QT as follows: << << s p o >> id val >> p' o'.
The AIDS Antiviral Screen dataset is a dataset of screens checking tens of thousands of compounds for evidence of anti-HIV activity. The available screen results are chemical graph-structured data of these various compounds. Source: Graph Neural Processes Towards Bayesian Graph Neural Networks
The Aristo Tuple KB contains a collection of high-precision, domain-targeted (subject,relation,object) tuples extracted from text using a high-precision extraction pipeline, and guided by domain vocabulary constraints. The dataset was introduced by the paper Domain-Targeted, High Precision Knowledge Extraction.
FinDKG: The Global Financial Dynamic Knowledge Graph Dataset FinDKG is an open-source dataset focused on creating a temporally-resolved Financial Dynamic Knowledge Graph. Designed to bridge the gap in industry-specific knowledge graphs, particularly in the financial sector, FinDKG provides a high-touch, temporally-aware representation of global economic and market dynamics. This repository includes comprehensive details about the dataset, methodology, and schema, aiming to facilitate academic research and actionable insights in global financial markets. ## Background While general-purpose knowledge graphs are abundant, industry-specific ones are comparatively rare, especially in the financial sector. FinDKG aims to fill this void by offering a resource for researchers and professionals looking to leverage knowledge graph technology in finance. ## FinDKG Dataset The dataset's foundation lies in an extensive news corpus curated to capture both qualitative and quantitative indicators in the financial landscape. We utilized the Wayback Machine to amass a dataset comprising global financial news. ## Dataset Structure - Temporal Knowledge Graph (TKG) with daily-resolved event triplets - Event triplets are tagged with specific timestamps corresponding to their release dates - Training, validation, and test splits organized chronologically - Weekly aggregation of event triplets as the basic unit of time ### Data Format /FinDKG is the default study dataset folder including the graph dataset and the corresponding data splits. The graph dataset is organized in the following structure: * 'train.txt', 'valid.txt', and 'test.txt': The first four columns correspond to subject, relation, object, and time. The fifth column is ignored. * 'stat.txt': The first two columns correspond to the number of entities and relations, respectively. Test set is held-out for evaluating the model performance. This should match the results of the original paper regarding the Temporal Link Prediction evaluation. /FinDKG-full: The full dataset including a larger size of the event triplets. This graph dataset adopts the same format as /FinDKG while is left for future extended research. * 'time2id.txt': This time mapping table further provided the mapping from time ID to realistic date for real-world application. ## Usage The dataset is designed for graph-based AI methods aiming to generate actionable insights in the financial domain. It is freely available for academic and research purposes. Refer details to our designated FinDKG website. (Description from Dataset Repo)
This repository is an extension of GEval. This repository contains a (software) evaluation framework to perform evaluation and comparison on RDF-star graph embedding techniques. The gold standard datasets for evaluation were created from KGRC-RDF-star. Please see here.
This dataset accompanies the paper `Learning the mechanisms of network growth' by the same authors. The dataset contains 6733 networks of size 20,000 each generated in accordance to different combination of three mechanisms: fitness, aging and preferential attachment. The goal is to use machine learning to identify the combination of mechanisms that was used to create the network. The dataset includes static features from the literature and two version of our newly developed dynamic features. net