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dc.contributor.authorMeng, Wang-
dc.contributor.authorYinghui, Shi-
dc.contributor.authorHan, Yang-
dc.date.accessioned2023-04-07T09:19:38Z-
dc.date.available2023-04-07T09:19:38Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s41019-023-00208-9-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/7685-
dc.descriptionCC BYvi
dc.description.abstractWe study the problem of multimodal embedding-based entity alignment (EA) between different knowledge graphs. Recent works have attempted to incorporate images (visual context) to address EA in a multimodal view. While the benefits of multimodal information have been observed, its negative impacts are non-negligible as injecting images without constraints brings much noise. It also remains unknown under what circumstances or to what extent visual context is truly helpful to the task.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectentity alignmentvi
dc.titleProbing the Impacts of Visual Context in Multimodal Entity Alignmentvi
dc.typeBookvi
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