Structural Modeling of Heterogeneous CAM Model Based on Process Knowledge Graph

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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Indoor Space Model Based on Knowledge Graph

2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) ◽

10.1109/ihmsc.2018.10123 ◽

2018 ◽

Author(s):

Hongjian Wu ◽

Xin Chou ◽

Guanyu Li ◽

Yimin Shi

Keyword(s):

Knowledge Graph ◽

Indoor Space ◽

Space Model ◽

Model Based

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Process Knowledge Graph Modeling Techniques and Application Methods for Ship Heterogeneous Models

10.21203/rs.3.rs-955940/v1 ◽

2021 ◽

Author(s):

Jinfeng Liu ◽

Jianwei Dong ◽

Xuwen Jing ◽

Xuwu Cao ◽

Chenxiao Du ◽

...

Keyword(s):

Process Design ◽

Welding Process ◽

Mapping Method ◽

Knowledge Graph ◽

Case Based Reasoning ◽

Test Model ◽

Process Knowledge ◽

Heterogeneous Model ◽

Heterogeneous Models ◽

Component Assembly

Abstract In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, a case study of the test model is carried out to verify the feasibility and efficiency of the proposed method.

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Construction of Power Communication Network Knowledge Graph with BERT-BiLSTM-CRF Model Based Entity Recognition

2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) ◽

10.1109/icccs52626.2021.9449229 ◽

2021 ◽

Author(s):

Haiyang Wu ◽

Peng Chen ◽

Wei Li ◽

Yong Dai ◽

Chunxia Jiang ◽

...

Keyword(s):

Communication Network ◽

Entity Recognition ◽

Knowledge Graph ◽

Model Based

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Recommendation Model Based on Semantic Features and a Knowledge Graph

Wireless Communications and Mobile Computing ◽

10.1155/2021/2382892 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yudong Liu ◽

Wen Chen

Keyword(s):

Semantic Similarity ◽

Information Science ◽

Absolute Error ◽

Knowledge Graph ◽

Semantic Features ◽

Short Text ◽

Model Based ◽

Recommendation Algorithms ◽

Tremendous Amount ◽

Series Of Experiments

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.

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Hybrid feature selection model based on machine learning and knowledge graph

Journal of Physics Conference Series ◽

10.1088/1742-6596/2079/1/012028 ◽

2021 ◽

Vol 2079 (1) ◽

pp. 012028

Author(s):

Xiaoqing Peng ◽

Yong Shuai ◽

Yaxi Gan ◽

Yaokai Chen

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Unsupervised Learning ◽

Supervised Learning ◽

Learning Algorithms ◽

Selection Model ◽

Knowledge Graph ◽

Model Based ◽

Text Features ◽

Learning Data

Abstract Aiming at the problem that the current feature selection algorithm can not adapt to both supervised learning data and unsupervised learning data, and had poor feature interpretability, this paper proposed a hybrid feature selection model based on machine learning and knowledge graph. By the idea of hybridization, this model used supervised learning algorithms, unsupervised learning algorithms and knowledge graph technology to model from the perspective of data features and text features. Firstly, the data-based feature weights were obtained through the machine learning model, and then the text-based weights were obtained by using the knowledge graph technology, and the weight sets are combined to obtain a feature matrix with good explanatory properties that meets both the data and text features. Finally, the case analysis proves that the method proposed in this paper has good effects and interpretability.

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Curriculum Semantic Retrieval System based on Distant Supervision

10.5121/csit.2021.111603 ◽

2021 ◽

Author(s):

Qingwen Tian ◽

Shixing Zhou ◽

Yu Cheng ◽

Jianxia Chen ◽

Yi Gao ◽

...

Keyword(s):

Semantic Network ◽

Relation Extraction ◽

Knowledge Graph ◽

Learning Approaches ◽

Semantic Retrieval ◽

Model Based ◽

Distant Supervision ◽

Relationship Extraction ◽

Extraction Model ◽

The Relationship

Knowledge Graph is a semantic network that reveals the relationship between entities, which construction is to describe various entities, concepts and their relationships in the real world. Since knowledge graph can effectively reveal the relationship between the different knowledge items, it has been widely utilized in the intelligent education. In particular, relation extraction is the critical part of knowledge graph and plays a very important role in the construction of knowledge graph. According to the different magnitude of data labeling, entity relationship extraction tasks of deep learning can be divided into two categories: supervised and distant supervised. Supervised learning approaches can extract effective entity relationships. However, these approaches rely on labeled data heavily resulting in the time-consuming and laborconsuming. The distant supervision approach is widely concerned by researchers because it can generate the entity relation extraction automatically. However, the development and application of the distant supervised approach has been seriously hindered due to the noises, lack of information and disequilibrium in the relation extraction tasks. Inspired by the above analysis, the paper proposes a novel curriculum points relationship extraction model based on the distant supervision. In particular, firstly the research of the distant supervised relationship extraction model based on the sentence bag attention mechanism to extract the relationship of curriculum points. Secondly, the research of knowledge graph construction based on the knowledge ontology. Thirdly, the development of curriculum semantic retrieval platform based on Web. Compared with the existing advanced models, the AUC of this system is increased by 14.2%; At the same time, taking "big data processing" course in computer field as an example, the relationship extraction result with F1 value of 88.1% is realized. The experimental results show that the proposed model provides an effective solution for the development and application of knowledge graph in the field of intelligent education.

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