2019
DOI: 10.1007/978-3-030-21348-0_30
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MMKG: Multi-modal Knowledge Graphs

Abstract: We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communities can benefit from this resource. We believe this data set has the potential to facilitate the development of novel multi-modal learning approaches for knowledge graphs. We validate the utility of MMKG in the sameAs link prediction task with an extensive set… Show more

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Cited by 134 publications
(68 citation statements)
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“…An entity could be associated with information in multiple modalities, such as texts, images, and even videos. To align such entities, the task of multi-modal entity alignment is worth further investigation [17]. -EA in the open world.…”
Section: Guidelines For Practitionersmentioning
confidence: 99%
“…An entity could be associated with information in multiple modalities, such as texts, images, and even videos. To align such entities, the task of multi-modal entity alignment is worth further investigation [17]. -EA in the open world.…”
Section: Guidelines For Practitionersmentioning
confidence: 99%
“…FB15k-237 is a subset of FB15k, which removes redundant relations in FB15k and greatly reduces the number of relations. We collect the multi-modal information of FB15k-237 according to [22]. As for the Symptoms-in-Chinese dataset, we following [56].…”
Section: Datasetsmentioning
confidence: 99%
“…Recent developments in machine learning (e.g., see [42,43]) propose approaches for scalable link predictions in complex KGs [44]. Even though these algorithms perform well on general purpose data, and they can be used, for example, in recommendation systems [9], none of them provide reliable solutions for link prediction based on geospatial information, especially in the context of multi-scale vector geometries.…”
Section: Five-star Data: Linkingmentioning
confidence: 99%