Personalized Recommendation of English Learning Based on Knowledge Graph and Graph Convolutional Network

Abstract Cybersecurity has gradually become the public focus between common people and countries with the high development of Internet technology in daily life. The cybersecurity knowledge analysis methods have achieved high evolution with the help of knowledge graph technology, especially a lot of threat intelligence information could be extracted with fine granularity. But named entity recognition (NER) is the primary task for constructing security knowledge graph. Traditional NER models are difficult to determine entities that have a complex structure in the field of cybersecurity, and it is difficult to capture non-local and non-sequential dependencies. In this paper, we propose a cybersecurity entity recognition model CyberEyes that uses non-local dependencies extracted by graph convolutional neural networks. The model can capture both local context and graph-level non-local dependencies. In the evaluation experiments, our model reached an F1 score of 90.28% on the cybersecurity corpus under the gold evaluation standard for NER, which performed better than the 86.49% obtained by the classic CNN-BiLSTM-CRF model.

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Research of Personalized Recommendation Technology Based on Knowledge Graphs

Applied Sciences ◽

10.3390/app11157104 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7104

Author(s):

Xu Yang ◽

Ziyi Huan ◽

Yisong Zhai ◽

Ting Lin

Keyword(s):

Neural Network ◽

Hot Spot ◽

Experimental Results ◽

Graph Representation ◽

Personalized Recommendation ◽

Knowledge Graph ◽

Learning Technology ◽

Data Set ◽

Model Based ◽

Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

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Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042044 ◽

2021 ◽

Vol 2083 (4) ◽

pp. 042044

Author(s):

Zuhua Dai ◽

Yuanyuan Liu ◽

Shilong Di ◽

Qi Fan

Keyword(s):

Neural Network ◽

Sentiment Analysis ◽

Structural Information ◽

Knowledge Graph ◽

Convolutional Network ◽

Text Data ◽

Short Text ◽

Fine Grained ◽

Syntactic Information ◽

Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

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Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420590338 ◽

2020 ◽

Vol 34 (10) ◽

pp. 2059033

Author(s):

Gang Huang ◽

Man Yuan ◽

Chun-Sheng Li ◽

Yong-he Wei

Keyword(s):

Collaborative Filtering ◽

Domain Knowledge ◽

Cold Start ◽

Petroleum Exploration ◽

Personalized Recommendation ◽

Knowledge Graph ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

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Multipath Cross Graph Convolution for Knowledge Representation Learning

Computational Intelligence and Neuroscience ◽

10.1155/2021/2547905 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Luogeng Tian ◽

Bailong Yang ◽

Xinli Yin ◽

Kai Kang ◽

Jing Wu

Keyword(s):

Representation Learning ◽

Knowledge Graph ◽

Baseline Model ◽

Convolutional Network ◽

Redundant Feature ◽

Feature Information ◽

Semantic Spaces ◽

Local Graph ◽

End To End ◽

Representation Method

In the past, most of the entity prediction methods based on embedding lacked the training of local core relationships, resulting in a deficiency in the end-to-end training. Aiming at this problem, we propose an end-to-end knowledge graph embedding representation method. It involves local graph convolution and global cross learning in this paper, which is called the TransC graph convolutional network (TransC-GCN). Firstly, multiple local semantic spaces are divided according to the largest neighbor. Secondly, a translation model is used to map the local entities and relationships into a cross vector, which serves as the input of GCN. Thirdly, through training and learning of local semantic relations, the best entities and strongest relations are found. The optimal entity relation combination ranking is obtained by evaluating the posterior loss function based on the mutual information entropy. Experiments show that this paper can obtain local entity feature information more accurately through the convolution operation of the lightweight convolutional neural network. Also, the maximum pooling operation helps to grasp the strong signal on the local feature, thereby avoiding the globally redundant feature. Compared with the mainstream triad prediction baseline model, the proposed algorithm can effectively reduce the computational complexity while achieving strong robustness. It also increases the inference accuracy of entities and relations by 8.1% and 4.4%, respectively. In short, this new method can not only effectively extract the local nodes and relationship features of the knowledge graph but also satisfy the requirements of multilayer penetration and relationship derivation of a knowledge graph.

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HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7249 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13953-13954

Author(s):

Xu Wang ◽

Shuai Zhao ◽

Bo Cheng ◽

Jiale Han ◽

Yingting Li ◽

...

Keyword(s):

Question Answering ◽

Binary Classification ◽

Experimental Results ◽

Ranking Method ◽

Knowledge Graph ◽

Convolutional Network ◽

Proposed Model ◽

The Right ◽

Novel Model ◽

Heterogeneous Knowledge

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.

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