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[Purpose/Significance] Breeding 4.0, characterized by "biotechnology + artificial intelligence + big data information technology," has brought new requirements for the digital management and intelligent utilization of germplasm resources. In order to meet the diverse support needs for knowledge service forms under an intelligent background, this article aims to propose an effective method for knowledge organization and deep semantic association. This is essential to address the inconveniences that discrete germplasm resource data bring to researchers when collaborating across regions and institutions. Therefore, the article presents a method that integrates fragmented domain data into a systematic knowledge system, which is particularly important. [Method/Process] By analyzing the domain data descriptions and the current organizational status, the ontology construction was performed using the seven-step method developed by Stanford University Hospital. First, existing ontologies such as the Crop Ontology, Gene Ontology, and Darwin Core were referenced and reused, and then integrated with the knowledge framework from the "Technical Specifications for Crop Germplasm Resources" series and example datasets. Consequently, an ontology model was successfully constructed, which covers five major categories of crops: cereals, cash crops, vegetables, fruit trees, and forage and green manure crops. This model defines 11 core classes including phenotypes and genotypes, as well as identification methods and evaluation standards, along with 10 object properties and 56 data properties. [Results/Conclusions] Based on the ontology model, the article proposes a methodology for constructing a knowledge graph of crop germplasm resources. Using rice as an example, a domain-specific fine-grained knowledge graph is developed to facilitate semantic association and querying across multiple knowledge dimensions. The article also outlines prospective designs for new intelligent knowledge service scenarios driven by the knowledge graph, such as intelligent question and answer and knowledge computation, aiming to meet the knowledge service needs of researchers, breeding companies, and the general public. This is intended to provide more accurate and efficient support for computational breeding efforts. Currently, the research focuses only on rice as an example of a cereal crop, with economic crops, vegetables, and other types of crop germplasm resources not yet included in the study. Future work will expand the scope of the study and add new classes and properties specific to different germplasm resources to better address the diverse and personalized knowledge needs of users in the eraa of big data. This approach aims to promote the contextualization, ubiquity, and intelligence of knowledge services, and to further integrate them into different academic disciplines related to the development of new quality digital productivity.

[Purpose/Significance] New quality productive forces is the latest pattern of productivity development under the background of data-driven intelligence. To explore the mechanism and practical way of bidirectional empowerment of knowledge service and new quality productive forces has important theoretical and practical significance for giving full play to the effectiveness of new quality of data elements, new power of digital intelligence technology and new application of intelligent service, so as to promote the cultivation of new quality productive forces and improve knowledge service. [Method/Process] Based on the summary of the development process of knowledge service and the new interpretation from the perspective of new quality productive forces, this study analyzes the internal relationship and external manifestations of the bidirectional empowerment of knowledge services and new quality productive forces, constructs the bidirectional empowerment mechanism of knowledge services and new quality productive forces, and proposes the bidirectional empowerment path. [Results/Conclusions] "Factor reorganization, scientific and technological innovation, and quality service" has become the internal relationship between knowledge service and new quality productive forces. Data factor as the core production factor, digital intelligence technology as the key production technology, and intelligence-driven new business forms and new models as the common carrier, which is an important basis for intelligent knowledge service and new quality productive forces bidirectional empowerment mechanism. Intelligent knowledge service cultivates the new quality productive forces through three important ways: accelerating scientific and technological innovation, opening up new ways and creating new business forms. New quality productive forces drives the development of intelligent knowledge service and improves the service by solving the resource dilemma, technology dilemma and talent dilemma, and creating new competitive advantages. This is an important manifestation of the bidirectional empowerment mechanism of new quality productive forces and intelligent knowledge service. After clarifying the empowerment basis and the performance of the bidirectional empowerment mechanism, this study proposes that the two kinds of empowerment should be realized from the following four aspects: a) Taking the market demand as the guide by mining new value and responding to new demand, and grasping the new quality growth engine; b) Taking data elements as the center, the compacting intelligent driving base is realized by building the data element empowerment system and improving the standardization of element utilization; c) Taking technological innovation as the key way by releasing the potential of key elements and creating fusion application scenarios to consolidate the foundation of fusion application; d) Starting from the application scenario as the starting point, the bidirectional empowerment mechanism is put into practice in various fields.

[Purpose/Significance] This study aims to complement the shortcomings existing in the current research, such as the limited research on the micro-level of enabling rural revitalization and the unclear impact of knowledge services on rural revitalization, in order to strengthen the understanding of the mechanism and path of enabling rural revitalization by agricultural intelligent knowledge services, and to provide theoretical reference and practical solutions for better enabling rural revitalization by the knowledge services and better developing agricultural intelligent knowledge services. [Method/Process] This paper first reviews the literature to understand the current research status of enabling rural revitalization and knowledge services affecting rural revitalization, and then determines the research focus. Based on the three perspectives of data, application and service, this paper explores the enabling basis of agricultural intelligent knowledge services, analyzes the enabling mechanism from the three aspects of agricultural production, farmer life and rural governance, clarifies the necessity and possibility of agricultural intelligent knowledge services enabling rural revitalization, and constructs the internal mechanism of the knowledge services enabling rural revitalization. Moreover, the predicament of agricultural intelligent knowledge services enabling rural revitalization is analyzed from the internal and external aspects, and the performance and impact of these dilemmas are analyzed. Finally, according to the analysis results, the optimization path is proposed from the three dimensions of enabling demand, scene and effect to alleviate the problem. [Results/Conclusions] Agricultural knowledge services have entered the intelligent stage, presenting the characteristics of intelligence, personalization and precision, and have established a good foundation for enabling rural revitalization in terms of data, application and service. By enabling the development of new production modes, the transformation of farmers' multiple identities, and the equal participation of multiple subjects in governance, digital intelligence momentum will be injected into the comprehensive promotion of rural revitalization. However, from the internal perspective of agricultural intelligent knowledge services, there are some shortcomings such as obvious user group bias and the lack of service content. From the external point of view of the knowledge services, there are the challenges of unbalanced infrastructure and low user literacy. These deficiencies and challenges limit the enabling role of agricultural intelligent knowledge services in rural revitalization, which will limit the maximum play of the enabling effects. Accordingly, this paper provides the corresponding solution. First, in-depth research could be carried out on rural revitalization stakeholders and strategic content to clarify the empowerment needs. Second, integrated service platform could be built to integrate and empower scenarios. Third, the promotion model could be constructed to enhance the enabling effect to alleviate the empowerment dilemma.

[Purpose/Significance] Argument mining, a research hotspot in the field of computational linguistics, provides machine processable structured data for computational models of argument. Argument mining tasks are closely related to artificial intelligence (AI) technologies, such as natural language processing and knowledge representation. There are numerous systematic studies in academia and a clear technical realization route has come into being. New research results continue to emerge as a result of rich resources and rapid development and iteration of deep learning, large language models (LLMs), and other technologies. This study, which reviews the research status and progress of argument mining, can serve as a resource for future research and application development. [Method/Process] Through literature review, this paper systematically reviews the relevant research basis (including foundational techniques and semantic representation models), summarizes the related technical system in terms of task framework, influencing factors of technological complexity, and method classification, and then introduces the argument mining practice and application cases for specific fields and research objectives and makes a comparative analysis. Most importantly, the overall development and characteristics of this research field are summarized, with a focus on tracking the progress of multimedia argument mining in the context of the new AI environment. [Results/Conclusions] Relevant research has experienced the development of "machine learning - deep learning" and "text only - multimodal", and the levels of development and application of various fields vary much. Future research may focus on how to achieve multigranularity and multimodal content generalization, as well as how to promote its application and implementation in practice. Possible research directions include: 1) the use of LLMs in argument mining, because they exhibit significant benefits in downstream applications such as natural language processing and multimodal learning, and can also provide certain technical conditions for the generation of argument content; 2) the use of domain knowledge organization systems such as vocabulary, knowledge base and knowledge graph: with these systems, researchers can combine domain-specific argument mining models with rich knowledge structure, to strengthen semantic representation and organization improve the systematization and dig deeper into argument mining model research in the domain; 3) promoting the application research and practice of argument mining in more fields or across disciplines, and improving the retrieval and visualization of argument information, such as combining information retrieval methods with argument mining to build the next generation of argument search engines.

[Purpose/Significance] In recent years, challenges such as pandemics, wars, and natural disasters have posed numerous threats to China's food security. As the core of future agricultural productivity improvement, the importance of the seed industry has been continuously emphasized by the government. To facilitate preservation and utilization, scholars have integrated and digitized a vast amount of germplasm resources. However, the current platforms of crop germplasm resource knowledge services still suffer from issues such as diverse and fragmented large-scale heterogeneous data sources, lack of interconnection among data, and insufficient exploration of the data, thereby falling short of achieving intelligent and semantic research on germplasm resources. Therefore, this article aims to propose an effective method for knowledge organization and semantic association to meet the growing demand for intelligent knowledge services from users. The proposed method is to provide insights into the development of germplasm resource knowledge service platforms tailored for computational breeding. [Method/Process] This paper conducted a comparative analysis by examining the description and organization of germplasm resource data domestically and internationally. Four mainstream international platforms of germplasm resource knowledge services were selected for comparison from five perspectives: general overview, resource quantity, the types of knowledge, retrieval methods, and results. The deficiencies of these platforms in intelligent services such as text mining, semantic retrieval, and knowledge computation were summarized. In general, these platforms still rely on keyword-based retrieval as the primary means of searching, lacking systematic modeling of germplasm resource knowledge and the ability to achieve semantic retrieval in an intelligent environment. However, with the development of information technology in the era of big data, there is a growing demand in China to promote the development of computational breeding and provide more accurate, faster, and more intelligent knowledge resources to researchers and ordinary farmers through AI-based germplasm resource knowledge services. Therefore, in response to these new demands, the article proposes the construction of a panoramic crop germplasm resource knowledge graph and the development of a knowledge graph-driven germplasm resource knowledge service platform. [Results/Conclusions] The knowledge graph provides a more efficient and intelligent form of knowledge organization, and a knowledge service platform based on the knowledge graph contributes to improved efficiency and accuracy of knowledge services. In the next step, this research will focus on building a large-scale germplasm resource knowledge graph based on germplasm resource data and expanding it with other data, such as genotype, phenotype, environmental, and literature information. The application exploration will be conducted in scenarios such as intelligent question answering and knowledge-based computational breeding.

[Purpose/Significance] Knowledge Graph has become a major research hotspot in the era of artificial intelligence due to its ability to provide a new means of organization and representation of knowledge. As the field continues to evolve, numerous scholars have proposed advanced algorithms and technologies for each core stage of constructing a knowledge graph, and many large domestic and foreign enterprises have also developed their independent knowledge graph management systems. However, the majority of these graph tools developed are designed for commercial use and are often too expensive and difficult to deploy locally for small and medium-sized research teams. This presents a challenge for information organizations such as research libraries with massive resources, which require a more adaptable, universal, and efficient tool to build and manage knowledge graphs. To meet this need, it is important to develop an open-source, user-friendly, and customizable knowledge graph management system that can be easily deployed by small and medium-sized research teams. [Method/Process] In summary, this article offers a thorough and informative analysis of six mainstream knowledge graph management systems, both domestically and internationally. It delves into the unique characteristics of each system within the business process and provides an in-depth comparative analysis based on several important factors, including system functionality, technology selection, open-source availability, and application domains. The article refers to the standard construction process of knowledge graphs and highlights the platform characteristics of each system during the construction process while also examining their limitations based on current data characteristics. In response to practical needs, the article focuses on multi-path, multi-engine, distributed, and collaborative construction, integrating advanced graph algorithms and considering a well-developed underlying graph storage strategy. [Results/Conclusions] As a result,the article presents an in-depth analysis of the construction model for a collaborative development and management system of an integrated knowledge graph. It not only investigates the current state of knowledge graph management systems but also proposes novel optimization ideas. These ideas include distributed collaborative construction, which allows for simultaneous contributions from multiple sources, and parallel management of multiple graphs, enabling efficient organization and retrieval. Additionally, some suggestions are put forward: developing multi-path knowledge extraction techniques to enhance the knowledge acquisition process, and using specialized multi-graph storage engines for optimized storage and retrieval. Last, the article emphasizes the importance of incorporating cross-media and multimodal knowledge into the graph for a comprehensive representation of information.

[Purpose/Significance] From the perspectives of strategic planning and implementation, this paper explores the development characteristics, development trend and practice path of agricultural knowledge service, and provides reference for the digitalization and intelligent development of agricultural knowledge service under the background of data-driven intelligence, and serves to support major national strategies such as rural revitalization and digital countryside construction. [Method/Process] In order to analyze the characteristics of agricultural knowledge service, this paper analyzes and summarizes the service from the perspectives of industry and discipline. In terms of industry, it focuses on the analysis of industry positioning of agricultural knowledge service, while in terms of discipline, the subject characteristics of agricultural knowledge service are analyzed. In order to grasp the development trend of agricultural knowledge service, this paper analyzes the service development course from the historical perspective and reveals the development trend. Finally, based on the above conclusions, combined with the needs of national strategy for agricultural knowledge service in supporting strategic development and serving rural construction, the development suggestions of agricultural knowledge service were put forward, including two levels of strategy and implementation. [Results/Conclusions] About the characteristics of agricultural knowledge service, under the perspective of industry, agricultural knowledge service takes knowledge service as the core; from the perspective of discipline, agricultural knowledge service is characterized with the service based on the agricultural field. From the development stage, agricultural knowledge service has gone through three stages. The first stage is the exploratory period of interwoven development with agricultural information service. The second stage is characterized with the explosive development of platformization. It is now entering the third stage - the stage of data-driven intelligence. From the perspective of the development trend, this stage is characterized with precision, intelligence, digitalization and knowledge. In order to promote the innovative development of agricultural knowledge service and better serve and support the national strategy, this paper puts forward development strategies, including the strategic level and the implementation level. At the strategic level, agricultural knowledge service should be guided by national strategies and policies, optimize the distribution of knowledge service, maximize the benefit of resource and service allocation, focus on the key points of national strategic development, and give priority to meet the key needs of national strategic development. At the implementation level, agricultural knowledge service should implement intra-industry and extra-industry cooperation, integrate superior resources, and promote the coordination between the service supply and the service demand, so as to build a harmonious knowledge service ecology.

[Purpose/Significance] This study is focused on the current popular "chatbot" ChatGPT to deepen users' overall cognition of ChatGPT, and provide reference and inspiration for the development of intelligent knowledge services in libraries by combining the power of new artificial intelligence (AI) technologies.[Method/Process] The article comprehensively analyzes ChatGPT, including its development history, technical features, common application scenarios, and integrated application program paths. In addition, it compares ChatGPT with similar AI technologies developed domestically and internationally (such as Google's Brad and Meta's BlenderBot 3), intuitively reflecting that new AI technologies such as pre-training models and cognitive intelligence have become the research and development focus of major technology institutions. The article also analyzes the technical limitations and existing security risks of ChatGPT, pointing out the optimization direction for secondary development and indicating its potential hazards for other researchers. Furthermore, the potential impact of ChatGPT on the Chinese libraries and information institutions are explored. By studying the application of ChatGPT in libraries and information service institutions, this article attempts to provide an in-depth understanding of how to use this technology to improve information retrieval, knowledge management, and user engagement. Finally, a comprehensive overview of ChatGPT and its potential impact on the Chinese information environment is provided. [Results/Conclusions] The integration of new technologies such as big data and AI has great potential for China's library and information service institutions to provide better and more intelligent knowledge services. With the development of modern technologies, libraries and information service institutions have been faced with new challenges and opportunities at the same time. The challenges come from the overwhelming amount of information, the diversification of information resources, and the increasing demands of users for personalized services. The opportunities arise from the availability of advanced technologies such as big data and AI that can help libraries and information service institutions to address these challenges. By fully integrating big data and AI into libraries and information service institutions, these institutions can leverage the power of these advanced technologies to provide more intelligent knowledge services. High-quality scientific and technological resources and knowledge organization systems can play a vital role in ensuring that these institutions are equipped with the necessary infrastructure and expertise to successfully implement these technologies. In conclusion, the integration of big data and AI represents a significant opportunity for China's libraries and information service institutions to provide better and more intelligent knowledge services. By relying on high-quality scientific and technological resources and knowledge organization systems, these institutions can comprehensively improve their level of intelligent knowledge services, and better meet the needs and demands of their users in the digital age.

[Purpose/Significance] To solve the problem of unbalanced supply and demand of agricultural Sci-Tech knowledge services in the big data environment, this paper proposed a "data + demand" driven knowledge service supply model. [Method/Process] The proposed model contains a knowledge service system model for different application scenarios and a technical supporting system with convergence of multi-source heterogeneous agricultural science and technology big data, knowledge computing and intelligent services as links. [Results/Conclusions] We developed a multi-scenario agricultural knowledge service system characterized as "Inclusive + Specialized + Customized", by addressing the key technologies of agricultural Sci-Tech big data integration and knowledge service, and thus provided differentiated, accurate and in-depth knowledge services for users of multiple types, levels and disciplines. The agricultural knowledge service system helps address the contradiction between supply and demand of knowledge services, and meet the need of inclusive & personalized knowledge service simultaneously to a certain extent.