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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The entity alignment of science and technology patents aims to link theequivalent entities in the knowledge graph of different science and technologypatent data sources. Most entity alignment methods only use graph neuralnetwork to obtain the embedding of graph structure or use attribute textdescription to obtain semantic representation, ignoring the process ofmulti-information fusion in science and technology patents. In order to makeuse of the graphic structure and auxiliary information such as the name,description and attribute of the patent entity, this paper proposes an entityalignment method based on the graph convolution network for science andtechnology patent information fusion. Through the graph convolution network andBERT model, the structure information and entity attribute information of thescience and technology patent knowledge graph are embedded and represented toachieve multi-information fusion, thus improving the performance of entityalignment. Experiments on three benchmark data sets show that the proposedmethod Hit@K The evaluation indicators are better than the existing methods.

Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion