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Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark.
Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.
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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
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.
1.0 Version of OpenEA benchmark datasets. Please use the updated 2.0 version, that has been subsequently released. 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.
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
DPB-5L is a Multilingual KG dataset containing 5 KGs in English, French, Japanese, Greek, and Spanish. 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.
A large-scale cross-lingual dataset for entity alignment
The DBP2.0 dataset can be downloaded from the figshare repository. It has three entity alignment settings, i.e., ZH-EN, JA-EN and FR-EN. Each setting has the following files: ent_links: reference entity alignment; rel_triples_1: relation triples in the ZH or JA or FR KG, list of triples like (h \t r \t t); rel_triples_2: relation triples in the EN KG; splits/train_links: training data for entity alignment, list of pairs like (e1 \t e2); splits/valid_links: validation data for entity alignment; splits/test_links: test data for entity alignment; splits/train_unlinked_ent1: training data for dangling entity detection, list of dangling entities in the ZH or JA or FR KG; splits/train_unlinked_ent2: training data for dangling entity detection, list of dangling entities in the EN KG; splits/valid_unlinked_ent1: validation data for dangling entity detection, list of dangling entities in the ZH or JA or FR KG; splits/valid_unlinked_ent2: validation data for dangling entity detection, list of dangling entities in the EN KG; splits/test_unlinked_ent1: test data for dangling entity detection, list of dangling entities in the ZH or JA or FR KG; splits/test_unlinked_ent2: test data for dangling entity detection, list of dangling entities in the EN KG; More information see: https://github.com/nju-websoft/OpenEA/tree/master/dbp2.0
EventEA is an event-centric entity alignment dataset, harvested from EventKG, DBpedia and Wikidata. Source: EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs Image Source: https://arxiv.org/pdf/2211.02817v1.pdf
This dataset is used for user identity linkage across two online social networks in Chinese. It contains two popular Chinese social platforms: Sina Weibo\footnote{https://weibo.com} and Douban\footnote{https://www.douban.com}. Details: * 9,714 users and 117,218 relations in Weibo; 9,526 users and 120,245 relations in Douban; 1,397 pair of matched users. * Approximate power-law degree distribution and high aggregation coefficient. * Multiple text attributes available, including username, geographical location and recent (text) posts of the users. * Construction time: April 2020.
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