Knowledge Graphs

The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations. Source: The MovieLens Datasets: History and Context Image Source: http://files.grouplens.org/papers/harper-tiis2015.pdf

ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. Source: ConceptNet 5.5: An Open Multilingual Graph of General Knowledge Image Source: Speer et al

The FB15k dataset contains knowledge base relation triples and textual mentions of Freebase entity pairs. It has a total of 592,213 triplets with 14,951 entities and 1,345 relationships. FB15K-237 is a variant of the original dataset where inverse relations are removed, since it was found that a large number of test triplets could be obtained by inverting triplets in the training set. Source: https://www.microsoft.com/en-us/download/details.aspx?id=52312 Image Source: http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf

The WN18 dataset has 18 relations scraped from WordNet for roughly 41,000 synsets, resulting in 141,442 triplets. It was found out that a large number of the test triplets can be found in the training set with another relation or the inverse relation. Therefore, a new version of the dataset WN18RR has been proposed to address this issue. Source: http://nlpprogress.com/english/relation_prediction.html

FB15k-237 is a link prediction dataset created from FB15k. While FB15k consists of 1,345 relations, 14,951 entities, and 592,213 triples, many triples are inverses that cause leakage from the training to testing and validation splits. FB15k-237 was created by Toutanova and Chen (2015) to ensure that the testing and evaluation datasets do not have inverse relation test leakage. In summary, FB15k-237 dataset contains 310,116 triples with 14,541 entities and 237 relation types.

WN18RR is a link prediction dataset created from WN18, which is a subset of WordNet. WN18 consists of 18 relations and 40,943 entities. However, many text triples are obtained by inverting triples from the training set. Thus the WN18RR dataset is created to ensure that the evaluation dataset does not have inverse relation test leakage. In summary, WN18RR dataset contains 93,003 triples with 40,943 entities and 11 relation types. Source: End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

Yet Another Great Ontology (YAGO) is a Knowledge Graph that augments WordNet with common knowledge facts extracted from Wikipedia, converting WordNet from a primarily linguistic resource to a common knowledge base. YAGO originally consisted of more than 1 million entities and 5 million facts describing relationships between these entities. YAGO2 grounded entities, facts, and events in time and space, contained 446 million facts about 9.8 million entities, while YAGO3 added about 1 million more entities from non-English Wikipedia articles. YAGO3-10 a subset of YAGO3, containing entities which have a minimum of 10 relations each. Source: Recent Advances in Natural Language Inference:A Survey of Benchmarks, Resources, and Approaches Image Source: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/downloads/

NELL is a dataset built from the Web via an intelligent agent called Never-Ending Language Learner. This agent attempts to learn over time to read the web. NELL has accumulated over 50 million candidate beliefs by reading the web, and it is considering these at different levels of confidence. NELL has high confidence in 2,810,379 of these beliefs. Source: A Survey on Knowledge Graphs: Representation, Acquisition and Applications Image Source: http://rtw.ml.cmu.edu/rtw/

ATOMIC is an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e.g., "if X pays Y a compliment, then Y will likely return the compliment"). Source: ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning Image Source: https://homes.cs.washington.edu/~msap/atomic/

CORD-19 is a free resource of tens of thousands of scholarly articles about COVID-19, SARS-CoV-2, and related coronaviruses for use by the global research community. Source: Allen Institute for AI

NELL-995 KG Completion Dataset

ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users recommend movies to each other. The dataset consists of over 10,000 conversations centered around the theme of providing movie recommendations. Source: Towards Deep Conversational Recommendations

The MetaQA dataset consists of a movie ontology derived from the WikiMovies Dataset and three sets of question-answer pairs written in natural language: 1-hop, 2-hop, and 3-hop queries. Source: https://arxiv.org/abs/1907.08176 Image Source: https://github.com/yuyuz/MetaQA

The Semantic Scholar corpus (S2) is composed of titles from scientific papers published in machine learning conferences and journals from 1985 to 2017, split by year (33 timesteps). Source: Learning Dynamic Author Representations with Temporal Language Models Image Source: [http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/] (http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/)

DBP15k contains four language-specific KGs that are respectively extracted from English (En), Chinese (Zh), French (Fr) and Japanese (Ja) DBpedia, each of which contains around 65k-106k entities. Three sets of 15k alignment labels are constructed to align entities between each of the other three languages and En. Source: Cross-lingual Entity Alignment for Knowledge Graphs with Incidental Supervision from Free Text

ComplexWebQuestions is a dataset for answering complex questions that require reasoning over multiple web snippets. It contains a large set of complex questions in natural language, and can be used in multiple ways: 1. By interacting with a search engine; 2. As a reading comprehension task: the authors release 12,725,989 web snippets that are relevant for the questions, and were collected during the development of their model; 3. As a semantic parsing task: each question is paired with a SPARQL query that can be executed against Freebase to retrieve the answer. Source: Allen Institute for AI Image Source: Talmor et al

OpenDialKG contains utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. Source: OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs

Jericho is a learning environment for man-made Interactive Fiction (IF) games. Source: Interactive Fiction Games: A Colossal Adventure

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. While MMKG is intended to perform relational reasoning across different entities and images, previous resources are intended to perform visual reasoning within the same image. The three knowledge graphs augmented with numerical features and images are called FB15k, YAGO15k, and DBPEDIA15k.

Contains around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in the dialogs require a larger subgraph of the KG. Source: Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

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. OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task. * 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).

Holl-E is a dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie. Source: Towards Exploiting Background Knowledge for Building Conversation Systems

RoboCup is an initiative in which research groups compete by enabling their robots to play football matches. Playing football requires solving several challenging tasks, such as vision, motion, and team coordination. Framing the research efforts onto football attracts public interest (and potential research funding) in robotics, which may otherwise be less entertaining to non-experts. Source: Robots as Actors in a Film: No War, A Robot Story Image Source: https://www.robocup.org/

One of the largest commonsense knowledge bases available, describing over 2 million disambiguated concepts and activities, connected by over 18 million assertions. Source: WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation

A large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Collects 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. Source: Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis

This dataset is a Wikipedia dump, split by relations to perform Few-Shot Knowledge Graph Completion. \begin{table}[] \begin{tabular}{@{}lllccl@{}} \textbf{Dataset} & \textbf{# Ent} & \textbf{# Rel} & \textbf{# Triplets} & \textbf{Train/Dev/Test} \ Wiki-One & 4,838,244 & 822 & 5,829,240 & 133/16/34 \ \end{tabular} \caption{Datasets used in the experiments. } \end{table}

This relational database consists of 24 unique names in two families (they have equivalent structures). Source: https://archive.ics.uci.edu/ml/datasets/kinship

A corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations---including not only comments and replies, but also their modifications, deletions and restorations---this data offers an unprecedented view of online conversation. Source: WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community

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. The structured contribution annotations are provided as (1) Contribution sentences : a set of sentences about the contribution in the article; (2) Scientific terms and relations: a set of scientific terms and relational cue phrases extracted from the contribution sentences; and (3) Triples: semantic statements that pair scientific terms with a relation, modeled toward subject-predicate-object RDF statements for KG building. The Triples are organized under three (mandatory) or more of twelve total information units (viz., ResearchProblem, Approach, Model, Code, Dataset, ExperimentalSetup, Hyperparameters, Baselines, Results, Tasks, Experiments, and AblationAnalysis).

ForecastQA is a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. The forecasting problem for this dataset is formulated as a restricted-domain, multiple-choice, question-answering (QA) task that simulates the forecasting scenario. Source: ForecastQA: A Question Answering Challenge for Event Forecasting

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

JerichoWorld is a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds. JerichoWorld provides 24,198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7,836 heldout instances over 9 additional games in the test set.

KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction). For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage: "Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756..." Source: KnowledgeNet

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. SemOpenAlex enables a broad range of use-case scenarios, such as * exploratory semantic search via our website, * large-scale scientific impact quantification, * other forms of scholarly big data analytics within and across scientific disciplines. * enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. * can serve for RDF query optimization benchmarks, * creating scholarly knowledge-guided language models, * as a hub for semantic scientific publishing.

We present a further analysis of visual modality incompleteness, benchmarking latest MMEA models on our proposed dataset MMEA-UMVM. To create our MMEA-UMVM(uncertainly missing visual modality) datasets, we perform random image dropping on MMEA datasets. Specifically, we randomly discard entity images to achieve varying degrees of visual modality missing, ranging from 0.05 to the maximum $R_{img}$ of the raw datasets with a step of 0.05 or 0.1. Finally, we get a total number of 97 data split. Refer to the following paper for more details: Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment

CoDEx comprises a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible but verified to be false. Source: CoDEx: A Comprehensive Knowledge Graph Completion Benchmark