English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Multimodal knowledge graph link prediction aims to improve the accuracy andefficiency of link prediction tasks for multimodal data. However, for complexmultimodal information and sparse training data, it is usually difficult toachieve interpretability and high accuracy simultaneously for most methods. Toaddress this difficulty, a new model is developed in this paper, namelyInterpretable Multimodal Knowledge Graph Answer Prediction via SequenceModeling (IMKGA-SM). First, a multi-modal fine-grained fusion method isproposed, and Vgg16 and Optical Character Recognition (OCR) techniques areadopted to effectively extract text information from images and images. Then,the knowledge graph link prediction task is modelled as an offlinereinforcement learning Markov decision model, which is then abstracted into aunified sequence framework. An interactive perception-based reward expectationmechanism and a special causal masking mechanism are designed, which "converts"the query into an inference path. Then, an autoregressive dynamic gradientadjustment mechanism is proposed to alleviate the insufficient problem ofmultimodal optimization. Finally, two datasets are adopted for experiments, andthe popular SOTA baselines are used for comparison. The results show that thedeveloped IMKGA-SM achieves much better performance than SOTA baselines onmultimodal link prediction datasets of different sizes.

IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling