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/
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
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
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
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
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
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'.
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.