A study on the construction of knowledge graph of Yunjin video resources under productive conservation

Lu, Lu; Li, Xu; Yuan, Gaoteng; Jing, Lei; Chen, Wei; Chen, Cheng

doi:10.1186/s40494-023-00932-5

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…Although this prior prediction technique (intra frame prediction) is used, the actual value is subtracted to calculate the residual. The residual matrix obtained in this way is easier to compress than the original data [26]. Yang improved the recognition algorithm by analyzing graph regularization and constructing a model, and compared and analyzed feature extraction methods.…”

Section: Frame Frame Framementioning

confidence: 99%

Construction of Sports Culture Recommendation Model Combining Big Data Technology and Video Semantic Comprehension

Xie,

Zhang

2023

IJACSA

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Information blast makes it harder for clients to channel the substance they are keen on. This study aims to combine big data and video semantic comprehension technology to realize the recommendation of sports culture videos by exploring the semantics of video and taking advantage of multisource heterogeneous information. The semantic structure of unstructured video data is defined first, and on this basis, Converse3D (C3D) -Connectionist Temporal Asifationon (CTC) is employed to complete the extraction of sub-action semantics and the integration of behaviour semantic sequences. In adjustment to break the botheration of low accurateness of the model for the semantic abstraction of unlabeled videos, this study proposes an unsupervised semantic abstraction adjustment based on Converse3D(C3D)-RAE, which completes the compression and affiliation of the semantic sequences and verifies the accurateness of both two models through experiments. In order to solve the problem of insufficient accuracy of video recommendation algorithms based on single video semantic similarity and topic similarity, this study comprehensively considers video semantic similarity and video topic similarity and proposes a multi-modal video recommendation algorithm. The experimental results show that the accuracy of the COMSIMbased algorithm is 7.8% higher than that of Video+ CNN + K-NearestNeighbor (KNN) and 15.9% higher than that of CLIP + CNN +Ncut+LDA.

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Section: Frame Frame Framementioning

confidence: 99%

Construction of Sports Culture Recommendation Model Combining Big Data Technology and Video Semantic Comprehension

Xie,

Zhang

2023

IJACSA

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“…It mainly includes entity recognition, attribute extraction, and relationship extraction [41]. The KG in this study is constructed based on the ontology framework described in previously published works [42]. This framework chose CIDOC CRM as the main ontology for construction and, based on the knowledge characteristics and intrinsic traits of Yunjin ontology, has reused core concepts from other ontology models like Time and Ma-ontology.…”

Section: Knowledge Extractionmentioning

confidence: 99%

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

Xu,

Lu,

Liu

2023

Herit Sci

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Nanjing Yunjin, one of China's traditional silk weaving techniques, is renowned for its unique local characteristics and exquisite craftsmanship, and was included in the Representative List of the Intangible Cultural Heritage of Humanity by UNESCO in 2009. However, with rapid development in weaving technology, ever-changing market demands, and shifting public aesthetics, Nanjing Yunjin, as an intangible cultural heritage, faces the challenge of survival and inheritance. Addressing this issue requires efficient storage, management, and utilization of Yunjin knowledge to enhance public understanding and recognition of Yunjin culture. In this study, we have constructed an intelligent question-answering system for Nanjing Yunjin digital resources based on knowledge graph, utilizing the Neo4j graph database for efficient organization, storage, and protection of Nanjing Yunjin knowledge, thereby revealing its profound cultural connotations. Furthermore, we adopted deep learning algorithms for natural language parsing. Specifically, we adopted BERT-based intent recognition technology to categorize user queries by intent, and we employed the BERT + BiGRU + CRF model for entity recognition. By comparing with BERT + BILSTM + CRF, BERT + CRF and BILSTM + CRF models, our model demonstrated superior performance in terms of precision, recall, and F1 score, substantiating the superiority and effectiveness of this model. Finally, based on the parsed results of the question, we constructed knowledge graph query statements, executed by the Cypher language, and the processed query results were fed back to the users in natural language. Through system implementation and testing, multiple indices including system response time, stability, load condition, accuracy, and scalability were evaluated. The experimental results indicated that the Nanjing Yunjin intelligent question-answering system, built on the knowledge graph, is able to efficiently and accurately generate answers to user’s natural language queries, greatly facilitating the retrieval and utilization of Yunjin knowledge. This not only reinforces the transmission, promotion, and application of Yunjin culture but also provides a paradigm for constructing other intangible cultural heritage question-answering systems based on knowledge graphs. This has substantial theoretical and practical significance for deeply exploring and uncovering the knowledge structure of human intangible heritage, promoting cultural inheritance and protection.

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Research on sports image classification method based on SE-RES-CNN model

Li,

Lei,

Ren

et al. 2024

Sci Rep

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As computer image processing and digital technologies advance, creating an efficient method for classifying sports images is crucial for the rapid retrieval and management of large image datasets. Traditional manual methods for classifying sports images are impractical for large-scale data and often inaccurate when distinguishing similar images. This paper introduces an SE module that adaptively adjusts the weights of input feature mapping channels, and a Res module that excels in deep feature extraction, preventing gradient vanishing, multi-scale processing, and enhancing generalization in image recognition. Through extensive experimentation on network structure adjustments, the SE-RES-CNN neural network model is applied to sports image classification. The model is trained on a sports image classification dataset from Kaggle, alongside VGG-16 and ResNet50 models. Training results show that the proposed SE-RES-CNN model improves classification accuracy by approximately 5% compared to VGG-16 and ResNet50 models. Testing revealed that the SE-RES-CNN model classifies 100 out of 500 sports images in 6 s, achieving an accuracy rate of up to 98% and a single prediction time of 0.012 s. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency.

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A study on the construction of knowledge graph of Yunjin video resources under productive conservation

Cited by 7 publications

References 35 publications

Construction of Sports Culture Recommendation Model Combining Big Data Technology and Video Semantic Comprehension

Construction of Sports Culture Recommendation Model Combining Big Data Technology and Video Semantic Comprehension

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

Research on sports image classification method based on SE-RES-CNN model

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