Multi-modal Entity Alignment

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