Link Prediction

The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data. Source: http://nlpprogress.com/english/sentiment_analysis.html Image Source: Maas et al

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

The PubMed dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words. Source: https://linqs.soe.ucsc.edu/data

The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. Source: https://ogb.stanford.edu/ Image Source: https://ogb.stanford.edu/

The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. Source: https://arxiv.org/pdf/1706.02216.pdf Image Source: https://minimaxir.com/2016/05/reddit-graph/

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 Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words. Source: https://relational.fit.cvut.cz/dataset/CORA Image Source: https://arxiv.org/abs/1611.08402

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

The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 3703 unique words. Source: https://linqs.soe.ucsc.edu/data

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/

protein roles—in terms of their cellular functions from gene ontology—in various protein-protein interaction (PPI) graphs, with each graph corresponding to a different human tissue [41]. positional gene sets are used, motif gene sets and immunological signatures as features and gene ontology sets as labels (121 in total), collected from the Molecular Signatures Database [34]. The average graph contains 2373 nodes, with an average degree of 28.8.

COLLAB is a scientific collaboration dataset. A graph corresponds to a researcher’s ego network, i.e., the researcher and its collaborators are nodes and an edge indicates collaboration between two researchers. A researcher’s ego network has three possible labels, i.e., High Energy Physics, Condensed Matter Physics, and Astro Physics, which are the fields that the researcher belongs to. The dataset has 5,000 graphs and each graph has label 0, 1, or 2. Source: 1 Introduction

The DBLP is a citation network dataset. The citation data is extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources. The first version contains 629,814 papers and 632,752 citations. Each paper is associated with abstract, authors, year, venue, and title. The data set can be used for clustering with network and side information, studying influence in the citation network, finding the most influential papers, topic modeling analysis, etc. Source: https://www.aminer.org/citation

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/

SNAP is a collection of large network datasets. It includes graphs representing social networks, citation networks, web graphs, online communities, online reviews and more. Social networks : online social networks, edges represent interactions between people Networks with ground-truth communities : ground-truth network communities in social and information networks Communication networks : email communication networks with edges representing communication Citation networks : nodes represent papers, edges represent citations Collaboration networks : nodes represent scientists, edges represent collaborations (co-authoring a paper) Web graphs : nodes represent webpages and edges are hyperlinks Amazon networks : nodes represent products and edges link commonly co-purchased products Internet networks : nodes represent computers and edges communication Road networks : nodes represent intersections and edges roads connecting the intersections Autonomous systems : graphs of the internet Signed networks : networks with positive and negative edges (friend/foe, trust/distrust) Location-based online social networks : social networks with geographic check-ins Wikipedia networks, articles, and metadata : talk, editing, voting, and article data from Wikipedia Temporal networks : networks where edges have timestamps Twitter and Memetracker : memetracker phrases, links and 467 million Tweets Online communities : data from online communities such as Reddit and Flickr Online reviews : data from online review systems such as BeerAdvocate and Amazon User actions : actions of users on social platforms. Face-to-face communication networks : networks of face-to-face (non-online) interactions Graph classification datasets : disjoint graphs from different classes Image Source: https://snap.stanford.edu/data/

NELL-995 KG Completion Dataset

The Yelp2018 dataset is adopted from the 2018 edition of the yelp challenge. Wherein local businesses like restaurants and bars are viewed as items. We use the same 10-core setting in order to ensure data quality.

A repository that contains political events with a specific timestamp. These political events relate entities (e.g. countries, presidents...) to a number of other entities via logical predicates (e.g. ’Make a visit’ or ’Express intent to meet or negotiate’).

Wiki-CS is a Wikipedia-based dataset for benchmarking Graph Neural Networks. The dataset is constructed from Wikipedia categories, specifically 10 classes corresponding to branches of computer science, with very high connectivity. The node features are derived from the text of the corresponding articles. They were calculated as the average of pretrained GloVe word embeddings (Pennington et al., 2014), resulting in 300-dimensional node features. The dataset has 11,701 nodes and 216,123 edges. Source: Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks Image Source: https://arxiv.org/pdf/2007.02901v1.pdf

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

Orkut is a social network dataset consisting of friendship social network and ground-truth communities from Orkut.com on-line social network where users form friendship each other. Each connected component in a group is regarded as a separate ground-truth community. The ground-truth communities which have less than 3 nodes are removed. The dataset also provides the top 5,000 communities with highest quality and the largest connected component of the network. Source: https://snap.stanford.edu/data/com-Orkut.html

We release Douban Conversation Corpus, comprising a training data set, a development set and a test set for retrieval based chatbot. The statistics of Douban Conversation Corpus are shown in the following table. | |Train|Val| Test | | ------------- |:-------------:|:-------------:|:-------------:| | session-response pairs | 1m|50k| 10k | | Avg. positive response per session | 1|1| 1.18 | | Fless Kappa | N\A|N\A|0.41 | | Min turn per session | 3|3| 3 | | Max ture per session | 98|91|45 | | Average turn per session | 6.69|6.75|5.95 | | Average Word per utterance | 18.56|18.50|20.74 | The test data contains 1000 dialogue context, and for each context we create 10 responses as candidates. We recruited three labelers to judge if a candidate is a proper response to the session. A proper response means the response can naturally reply to the message given the context. Each pair received three labels and the majority of the labels was taken as the final decision. <br> As far as we known, this is the first human-labeled test set for retrieval-based chatbots. The entire corpus link https://www.dropbox.com/s/90t0qtji9ow20ca/DoubanConversaionCorpus.zip?dl=0

The AMiner Dataset is a collection of different relational datasets. It consists of a set of relational networks such as citation networks, academic social networks or topic-paper-autor networks among others.

The Long Range Graph Benchmark (LRGB) is a collection of 5 graph learning datasets that arguably require long-range reasoning to achieve strong performance in a given task. The 5 datasets in this benchmark can be used to prototype new models that can capture long range dependencies in graphs. | Dataset | Domain | Task | |---|---|---| | PascalVOC-SP| Computer Vision | Node Classification | | COCO-SP | Computer Vision | Node Classification | | PCQM-Contact | Quantum Chemistry | Link Prediction | | Peptides-func | Chemistry | Graph Classification | | Peptides-struct | Chemistry | Graph Regression |

Wikidata5m is a million-scale knowledge graph dataset with aligned corpus. This dataset integrates the Wikidata knowledge graph and Wikipedia pages. Each entity in Wikidata5m is described by a corresponding Wikipedia page, which enables the evaluation of link prediction over unseen entities. The dataset is distributed as a knowledge graph, a corpus, and aliases. We provide both transductive and inductive data splits used in the original paper.

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.

STRING is a collection of protein-protein interaction (PPI) networks.

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/

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).

Bio-decagon is a dataset for polypharmacy side effect identification problem framed as a multirelational link prediction problem in a two-layer multimodal graph/network of two node types: drugs and proteins. Protein-protein interaction network describes relationships between proteins. Drug-drug interaction network contains 964 different types of edges (one for each side effect type) and describes which drug pairs lead to which side effects. Lastly, drug-protein links describe the proteins targeted by a given drug. The final network after linking entity vocabularies used by different databases has 645 drug and 19,085 protein nodes connected by 715,612 protein-protein, 4,651,131 drug-drug, and 18,596 drug-protein edges. Source: Modeling polypharmacy side effects with graph convolutional networks Image Source: Modeling polypharmacy side effects with graph convolutional networks

EmailEU is a directed temporal network constructed from email exchanges in a large European research institution for a 803-day period. It contains 986 email addresses as nodes and 332,334 emails as edges with timestamps. There are 42 ground truth departments in the dataset. Source: gl2vec: Learning Feature Representation Using Graphlets for Directed Networks

YAGO3-10 is benchmark dataset for knowledge base completion. It is a subset of YAGO3 (which itself is an extension of YAGO) that contains entities associated with at least ten different relations. In total, YAGO3-10 has 123,182 entities and 37 relations and 1,179,040 triples, and most of the triples describe attributes of persons such as citizenship, gender, and profession.

The Unified Medical Language System (UMLS) is a comprehensive resource that integrates and disseminates essential terminology, classification standards, and coding systems. Its purpose is to foster the creation of more effective and interoperable biomedical information systems and services, including electronic health records. Here are the key aspects of the UMLS: 1. Integration of Vocabularies: The UMLS brings together various health and biomedical vocabularies and standards. It acts as a bridge, enabling interoperability between different computer systems by harmonizing terminology. 2. Components: - Metathesaurus: This component contains terms and codes from multiple vocabularies, including CPT, ICD-10-CM, LOINC, MeSH, RxNorm, and SNOMED CT. It provides hierarchies, definitions, relationships, and other attributes. - Semantic Network: The Semantic Network defines broad categories (semantic types) and their relationships (semantic relations). - SPECIALIST Lexicon and Lexical Tools: These tools include a large syntactic lexicon for biomedical and general English. They assist in normalizing strings, generating lexical variants, and creating indexes. 3. Use Cases: - Clinical Practice: Link terms and codes across healthcare entities (doctors, pharmacies, insurance companies). - Patient Care Coordination: Facilitate communication among hospital departments. - Text Processing: Extract concepts, relationships, or knowledge from medical texts. - Terminology Mapping: Map between different terminologies. - Local Terminology Development: Create and maintain local terminologies. - Research: Investigate terminologies or ontologies. 4. Accessing the UMLS: - Web Browsers: You can search and explore UMLS data using UTS applications like the Metathesaurus Browser (retrieve concept information) and the Semantic Network Browser (view semantic types and relations). - Local Installation: Download UMLS files and use the MetamorphoSys tool to customize the UMLS for your specific needs. Load the customized data into your own database system or browse it using the MetamorphoSys RRF browser. Source: Conversation with Bing, 3/18/2024 (1) Unified Medical Language System (UMLS) - National Library of Medicine. https://www.nlm.nih.gov/research/umls/index.html. (2) UMLS Metathesaurus Browser. https://uts.nlm.nih.gov/uts/umls/home. (3) GitHub - dongwookim-ml/kg-data: knowledge-graph datasets. https://github.com/dongwookim-ml/kg-data. (4) UMLS Dataset | Papers With Code. https://paperswithcode.com/dataset/umls.

This dataset was collected and prepared by the CALO Project (A Cognitive Assistant that Learns and Organizes). It contains data from about 150 users, mostly senior management of Enron, organized into folders. The corpus contains a total of about 0.5M messages. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation.