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[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. [ABSTRACT FROM AUTHOR]

[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. [ABSTRACT FROM AUTHOR]

[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. [ABSTRACT FROM AUTHOR]