…The logical forms show diversified graph structure of free schema, which poses great challenges on the model's ability to understand the semantics.
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This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).
33 PAPERS • 6 BENCHMARKS
…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.
4 PAPERS • 1 BENCHMARK
BeGin provides 23 benchmark scenarios for graph from 14 real-world datasets, which cover 12 combinations of the incremental settings and the levels of problem.
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…PATTERN tests the fundamental graph task of recognizing specific predetermined subgraphs.
122 PAPERS • 1 BENCHMARK
…Introduced by "A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs by Sun et. al, VLDB 2020" Contains entities from DBpedia, YAGO and Wikidata.
6 PAPERS • 4 BENCHMARKS
FrameNet is a linguistic knowledge graph containing information about lexical and predicate argument semantics of the English language.
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PubMedCite is a domain-specific dataset with about 192K biomedical scientific papers and a large citation graph preserving 917K citation relationships between them.
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…In addition to the graph of ground weather stations, the dataset contains hourly readings of those stations. Readings include temperatures, wind characteristics, rain, and other information.
…It constitutes one of the first large datasets of an evolving knowledge graph, a recently emerging research subject in the Semantic Web community.
The CHILI-3K dataset is a medium-scale graph dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types.
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…It was created by (a) generating questions with multiple answers from Wikipedia's knowledge graph and tables, (b) automatically pairing answers with supporting evidence in Wikipedia paragraphs, and (c)
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…for coarse prediction are provided, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where the non-photographic class is broken down into five classes: maps, drawings, graphs
…Dialogues are distilled by contextualizing social commonsense knowledge from a knowledge graph (Atomic10x).
…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.
…RoMQA contains clusters of questions that are derived from related constraints mined from the Wikidata knowledge graph.
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KGRC-RDF-star is an RDF-star dataset converted from KGRC-RDF, which is a Knowledge graph dataset of novel stories. 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 corpus was converted into the native format of the annotation software GraphAnno and POS-tagged using the Stanford bidirectional dependency network tagger.
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…From paper: A context-aware citation recommendation model with BERT and graph convolutional networks
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This dataset is a Wikipedia dump, split by relations to perform Few-Shot Knowledge Graph Completion.
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…NeurIPS Dataset Track 2023 [2] FedRule: Federated Rule Recommendation System with Graph Neural Networks. IoTDI 2023
…It has been built by manually examining the 2-hop link existing in the knowledge graph of TREx-1p, and select eight 2- hop relation types that make sense to humans
VANiLLa is a dataset for Question Answering over Knowledge Graphs (KGQA) offering answers in natural language sentences.
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JerichoWorld is a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. 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
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…A bipartite graph connecting entities and documents is first built and the answer for each query is located by traversal on this graph.
67 PAPERS • 2 BENCHMARKS
…This website is meant to be browsed both by humans and machines alike, and can also be accessed via a convenient JSON API, or via the graph-tool library. The network datasets themselves are available in several machine-readable formats, in particular gt, GraphML, GML and CSV.
…Input graphs are used to represent chemical compounds, where vertices stand for atoms and are labeled by the atom type (represented by one-hot encoding), while edges between vertices represent bonds between
248 PAPERS • 3 BENCHMARKS
ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges.
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…In HoVer, the claims require evidence to be extracted from as many as four English Wikipedia articles and embody reasoning graphs of diverse shapes.
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VirtualHome2KG is a system for constructing and augmenting knowledge graphs (KGs) of daily living activities using virtual space. We also provide an ontology to describe the structure of the KGs.
The CHILI-100K dataset is a large-scale graph dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures.
…Along with the road network graph, it includes trip records represented as sequences of visited nodes (making the dataset suitable both for path-blind and path-aware settings).
2 PAPERS • 1 BENCHMARK
…Each AMR is a single rooted, directed graph. AMRs include PropBank semantic roles, within-sentence coreference, named entities and types, modality, negation, questions, quantities, and so on.
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This dataset comprises high-quality, targeted spear-phishing emails created using a proprietary system that harnesses the power of LLMs and knowledge graphs.
…We introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge.
…The original files are originally split into train/test split, while other research efforts (https://github.com/2003pro/Graph2Tree) perform the train/dev/test split.
89 PAPERS • 1 BENCHMARK
…Traffic Accident Prediction (TAP) data repository offers extensive coverage for 1,000 US cities (TAP-city) and 49 states (TAP-state), providing real-world road structure data that can be easily used for graph-based machine learning methods such as Graph Neural Networks.
…Each instance is an undirected graph. Track 1 is the "exact with low number of terminals" track, Track 2 is the "exact with low treewidth track", and Track 3 is the heuristic track. Graphs have sizes (number of vertices) ranging up to several thousand for exact tracks (1 and 2), and up to tens of thousands for the heuristic track.
…The data is derived from the SoMeSci Knowledge Graph of software mentions. Subtask 1 deals with the recognition of software mentions and the classification of mention (e.g. Krüger, “SoMeSci—A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles,” in Proceedings of the 30th ACM International Conference on Information and Knowledge Management
…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.
205 PAPERS • 5 BENCHMARKS
…It can be used as a longitudinal dataset for benchmarking the predictive performance of spatiotemporal graph neural network architectures.
…The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. A detailed description of the JetClass dataset is presented in the paper Particle Transformer for Jet Tagging.
9 PAPERS • 1 BENCHMARK
UMLS-43 is a variant of the UMLS knowledge graph that is robust to data leakage through inverse relations.
…code tokens, ASTs, graphs), and several properties (e.g., metrics, static analysis results) for 50,000 Java projects from the 50KC dataset, with over 1.2 million classes and over 8 million methods.
…By providing it open source (see License), we aim to motivate, support, and increase the application of database and knowledge graphs principles and techniques to the study of computational aspects of
…We construct a multi-relation graph based on the supplier, customer, shareholder, and financial information disclosed in the financial statements of Chinese companies.
…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.
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…We here abstract the problem into a new benchmark for node classification in a geo-referenced graph. Solving it requires learning the spatial layout of the organ including symmetries.