Multimodal Sentiment Analysis Based on Nonverbal Representation Optimization Network and Contrastive Interaction Learning

Quan, Zhibang; Sun, Tao; Su, Mengli; Wei, Jishu; Zhang, Xiang; Zhong, Shenjie

doi:10.1109/smc53654.2022.9945514

Cited by 3 publications

(1 citation statement)

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“…Many large knowledge graphs, such as YAGO [ 2 ], Freebase [ 3 ], and DBpedia [ 4 ], use triples to store the entities and relations of the knowledge base. With the advent of the era of artificial intelligence, knowledge graphs have been heavily used, such as critical resources for intelligent applications such as intelligent question answering [ 5 ], web search [ 6 ], recommender system [ 7 ], and sentiment analysis [ 8 , 9 ]. Figure 1 is an example of a simple knowledge graph.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Knowledge Graph Link Prediction Model Based on Relational Memory

Shi

Jing

2023

Computational Intelligence and Neuroscience

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A knowledge graph is a collection of fact triples, a semantic network composed of nodes and edges. Link prediction from knowledge graphs is used to reason about missing parts of triples. Common knowledge graph link prediction models include translation models, semantics matching models, and neural network models. However, the translation models and semantic matching models have relatively simple structures and poor expressiveness. The neural network model can easily ignore the overall structural characteristics of triples and cannot capture the links between entities and relations in low-dimensional space. In response to the above problems, we propose a knowledge graph embedding model based on a relational memory network and convolutional neural network (RMCNN). We encode triple embedding vectors using a relational memory network and decode using a convolutional neural network. First, we will obtain entity and relation vectors by encoding the latent dependencies between entities and relations and some critical information and keeping the translation properties of triples. Then, we compose a matrix of head entity encoding embedding vector, relation encoding embedding vector, and tail entity embedding encoding vector as the input of the convolutional neural network. Finally, we use a convolutional neural network as the decoder and a dimension conversion strategy to improve the information interaction capability of entities and relations in more dimensions. Experiments show that our model achieves significant progress and outperforms existing models and methods on several metrics.

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