The representation learning of knowledge graph aims to represent the semantic information of entities and relations as dense low-dimensional real-valued vectors and map them to the same low-dimensional space. Existing methods often focus on the single-modal information of the text and ignore the information of the image modality, resulting in the ineffective use of entity feature information in the image. And there is entity-related descriptive information in most knowledge graphs, which are not well used in current multimodal knowledge representation learning methods. In this regard, a multimodal knowledge representation learning method combining description information is proposed. This method combines multimodal (image, text) data to construct a knowledge representation learning model and combines its corresponding brief description information to improve the representation effect of multimodal data. Experimental results show that the method performs well on triple classification and link prediction tasks on the constructed WI-D dataset.
KEYWORDS: Field programmable gate arrays, Data processing, Computing systems, Data centers, Raster graphics, Control systems, Data transmission, Convolution, Telecommunications
The ever-increasing amount of data has brought severe pressure to the data center. Facing the large-scale data processing requirements, the data center not only needs to increase the data bandwidth, but also needs to ensure the timeliness of data processing. It is increasingly unable to meet the processing requirements of high throughput and low latency. This topic adopts the stream processing architecture of heterogeneous collaborative computing based on FPGA and CPU, designs a dual-channel separated stream processing system based on FPGA network offload processing, improves the management and interaction capabilities of FPGA, reduces processing delay, and provides a set of manageable The general data real-time processing platform.
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