With the escalating complexity in production scenarios, vast amounts of production information are retained within enterprises in the industrial domain. Probing questions of how to meticulously excavate value from complex document information and establish coherent information links arise. In this work, we present a framework for knowledge graph construction in the industrial domain, predicated on knowledge-enhanced document-level entity and relation extraction. This approach alleviates the shortage of annotated data in the industrial domain and models the interplay of industrial documents. To augment the accuracy of named entity recognition, domain-specific knowledge is incorporated into the initialization of the word embedding matrix within the bidirectional long short-term memory conditional random field (BiLSTM-CRF) framework. For relation extraction, this paper introduces the knowledge-enhanced graph inference (KEGI) network, a pioneering method designed for long paragraphs in the industrial domain. This method discerns intricate interactions among entities by constructing a document graph and innovatively integrates knowledge representation into both node construction and path inference through TransR. On the application stratum, BiLSTM-CRF and KEGI are utilized to craft a knowledge graph from a knowledge representation model and Chinese fault reports for a steel production line, specifically SPOnto and SPFRDoc. The F1 value for entity and relation extraction has been enhanced by 2% to 6%. The quality of the extracted knowledge graph complies with the requirements of real-world production environment applications. The results demonstrate that KEGI can profoundly delve into production reports, extracting a wealth of knowledge and patterns, thereby providing a comprehensive solution for production management.

Amidst the inefficiencies of traditional job-seeking approaches in the recruitment ecosystem, the importance of automated job recommendation systems has been magnified. However, existing models optimized to maximize user clicks for general product recommendations prove inept in addressing the unique challenges of job recommendation, namely reciprocity and competition. Moreover, sparse data on online recruitment platforms can further negatively impact the performance of existing job recommendation algorithms. To counteract these limitations, we propose a bilateral heterogeneous graph-based competition iteration model. This model comprises three integral components: 1) two bilateral heterogeneous graphs for capturing multi-source information from people and jobs and alleviating data sparsity, 2) fusion strategies for synthesizing attributes and preferences to produce mutually beneficial job matches, and 3) a competition-enhancing strategy for dispersing competition realized through a two-stage optimization algorithm. Augmented by granular attention mechanisms for enhanced interpretability, the model’s efficacy, competition dispersion, and interpretability are validated through rigorous empirical evaluations on a real-world recruitment platform.

Given the aging society, an increase in social demand, information- and communication technology-driven culture, and government policy support emerges to enable the development of the socialized care services system for the aged (SCSSA). The development of the SCSSA would be a significant step toward addressing China’s aging population. However, the construction of the SCSSA challenges the theories and methods of traditional elderly care service system construction. Specifically, the implementation path for such elderly care service policies is unclear, the necessary technological support is insufficient, and the mechanism for integrating intelligent information technology remains underexplored. Thus, this paper focuses on the needs of the elderly, grounded in the context of the changing elderly care service policies in China, and proposes a research paradigm that integrates system construction and support measure embedding. We then construct the original SCSSA, which includes “material + spirit + medical treatment + healthcare” and propose a method of optimization and iteration. Finally, we build the research framework of systematic support measures from the perspectives of policy reconstruction, institutional embeddedness, and technical support. Our work provides theoretical support and practical guidance for the construction and dynamic optimization of the SCSSA, thus making a significant contribution that will help China effectively cope with its aging society.

A smart society is an advanced form of society following agricultural society, industrial society, and information society, with digital data processing system as its main carrier. However, the governance of a smart society still faces many challenges. In view of this problem, first, this research constructs a smart society governance modernization strategy. Second, the innovation mode of a society governance mechanism driven by digital technology is proposed, including the precise intellectual control of a digital twin, the intelligent ubiquitous sensing of the Internet of Things, the empowerment remodeling of a blockchain and the livelihood service of artificial intelligence. Third, this study systematically explores the practice of smart society governance modernization from the aspects of basic information platform construction, evaluation system construction, application demonstration of epidemic prevention and control driven by big data, support of spatial intelligence and artificial intelligence technology for people’s livelihood, smart campus, public resources, and data governance application demonstration to provide theoretical guidance for promoting digital technology innovation in the process of the governance of a smart society.