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In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

The financial risk warning methods for enterprises have always been a practical concern. In digital society, the computational intelligence has brought more spirit to this demand. This paper first introduces the current situation of the development of Knowledge graph technology, describes the deep learning fusion method based on Knowledge graph, and expresses the feasibility of this study. Then, according to the requirements of Knowledge graph, it completes the method fusion of core data training and extraction, and completes the adaptive deep learning design for the Beautiful SCOP database, and establishes a STDE-FG financial risk early warning model. Through empirical analysis, the shortcomings of this model were identified, and a comparison of optimized and optimized results was completed. Two aspects of phenomenon can be found from experimental results. For one thing, the accuracy of the unoptimized STDE-FG early warning model has been improved by 37.5–55.3% compared to traditional prediction models, but the prediction value during legal person changes has a greater error than traditional prediction values. For another, the optimized STDE-FG early warning model has also improved its accuracy in predicting new investments and equity changes, with improvements of 17–32% and 16–28%, respectively, with significant changes. This model will have a positive impact on improving enterprise risk management capabilities and reducing financial risk costs. [ABSTRACT FROM AUTHOR]

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning method for image classification that leverages knowledge formalized as a graph created from information represented by pairs attribute/value. The proposed method investigates a loss function that adaptively combines the classical cross-entropy commonly used in deep learning with a novel penalty function. The novel loss function is derived from the representation of nodes after embedding the knowledge graph and incorporates the proximity between class and image nodes. Its formulation enables the model to focus on identifying the boundary between the most challenging classes to distinguish. Experimental results on several image databases demonstrate improved performance compared to state-of-the-art methods, including classical deep learning algorithms and recent algorithms that incorporate knowledge represented by a graph. [ABSTRACT FROM AUTHOR]

We develop a four-stage computational framework that takes the figures, tables and geological texts from mineral exploration reports as the input and outputs the constructed associated graphs and knowledge graphs for further semantic search. Figure 3 demonstrates the overall architecture of this framework. Figure 3. The basic framework of the proposed approach. The stage of pre-processing aims to split sentences, words and matching stop words for text information. The stage of diagram and corresponding descriptions of extraction aims to acquire images and extract image description information. The stage of table and corresponding descriptions of extraction aims to acquire table and extract table description information. Finally, we build a correlation model to relate the image - text - table to form knowledge to provide services. [Display omitted] • Visualize the geological contents with individual reports via a topic graph based on the LDA model. • Geological knowledge graph constructed based on geological reports. • Provides keyword-based queries and queries based on semantic information. Large-scale mineral resource reports offer a wealth of information for geological knowledge mining and mineral explorers' knowledge discovery. The geological conditions in which mineral deposits develop may be learned a great deal from these mineral exploration reports. Mineral exploration data may be queried and aggregated to effectively mitigate future exploration risks and reduce costs. However, due to the reports being presented in unstructured textual format, it becomes challenging to extract valuable geological data without manually scanning through a vast number of reports. This laborious process poses difficulties for geologists. To address this issue, this paper proposes a system that extracts a set of geologically relevant keywords/keyphrases of each chapter from each mineral exploration reposts using latent Dirichlet allocation (LDA), develops a topic graph, recognizes the geological entity and related relations for constructing a knowledge graph, and uses visualization of those graphs (e.g., topic graphs and knowledge graphs) to explore the contents of the report. The text mining and machine learning technique described here serves as the foundation for future research into incorporating semantic analysis into geological information extraction. The findings of this study show how automated text analysis may help with the quick processing of huge quantities of reports in order to identify target mineral systems and their related geological location and rock mineral composition. The suggested approaches can quickly and reliably convert mineral exploration data (e.g., text, figure, and table) into a structured form, which is a hitherto untouched field in geological knowledge mining. [ABSTRACT FROM AUTHOR]