13 research outputs found

    Restarted Hessenberg method for solving shifted nonsymmetric linear systems

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    It is known that the restarted full orthogonalization method (FOM) outperforms the restarted generalized minimum residual (GMRES) method in several circumstances for solving shifted linear systems when the shifts are handled simultaneously. Many variants of them have been proposed to enhance their performance. We show that another restarted method, the restarted Hessenberg method [M. Heyouni, M\'ethode de Hessenberg G\'en\'eralis\'ee et Applications, Ph.D. Thesis, Universit\'e des Sciences et Technologies de Lille, France, 1996] based on Hessenberg procedure, can effectively be employed, which can provide accelerating convergence rate with respect to the number of restarts. Theoretical analysis shows that the new residual of shifted restarted Hessenberg method is still collinear with each other. In these cases where the proposed algorithm needs less enough CPU time elapsed to converge than the earlier established restarted shifted FOM, weighted restarted shifted FOM, and some other popular shifted iterative solvers based on the short-term vector recurrence, as shown via extensive numerical experiments involving the recent popular applications of handling the time fractional differential equations.Comment: 19 pages, 7 tables. Some corrections for updating the reference

    A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops

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    Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes

    Dual Characteristics, Practical Prospect and Development Strategy of Agricultural Knowledge Service under the Background of Data-driven Intelligence

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    [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

    Comparison and Enlightenment of Crop Germplasm Resource Knowledge Service Platforms

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    [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

    Research Advances in Argument Mining

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    [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

    Insights and Reflections of the Impact of ChatGPT on Intelligent Knowledge Services in Libraries

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    [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

    Restarted Hessenberg method for shifted nonsymmetric linear systems with applications to fractional differential equations

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    It is known that the restarted full orthogonalization method (FOM) outperforms the restarted generalized minimum residual method (GMRES) in several circumstances for solving shifted linear systems when the shifts are handled simultaneously. Variants of them have been proposed to enhance their performance. We show that another restarted method, restarted Hessenberg method [M. Heyouni, Methode de Hessenberg Generalisee et Applications, Ph.D. Thesis, Universite des Sciences et Technologies de Lille, France, 1996] based on Hessenberg process, can effectively be employed, which can provide accelerating convergence rate with respect to the number of restarts. Theoretical analysis shows that the new residual of shifted restarted Hessenberg reduction method is still collinear with each other. In these cases where our proposed algorithm needs less enough number of restarts to converge than the earlier established restarted shifted FOM and weighted restarted shifted FOM, the associated CPU consuming time is also considerably reduced, as shown via extensive numerical experiments involving the recent popular applications of handling structural dynamics, time-fractional convection-diffusion equations and space-fractional diffusion equations

    Comparative Study and Optimization Strategies of Knowledge Graph Construction Management Systems

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    [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

    Construction of a domain knowledge service system based on the STKOS

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    Purpose: The study was carried out to construct a domain knowledge service system based on the Scientific &amp; Technological Knowledge Organization Systems (STKOS). Design/methodology/approach: The framework of a domain knowledge service system is designed on the basis of the STKOS, and the STKOS science and technology vocabularies, category systems, and ontology networks are applied to realize the knowledge organization and semantic linking of the scientific and technological information resources. Meanwhile, related knowledge-mining analysis algorithms and models are improved, and some tools such as Solr and D3 are used for developing the system. This system integrates various knowledge service modules, including unified search of domain information resources and knowledge-linked navigation, domain hotspot and burst topics monitoring analysis, knowledge structure and evolution analysis, literature citation network, and research agents&#39; cooperative relationship network analysis. Findings: The system can help to refine descriptions, knowledge organization, and the semantic linking of various kinds of information resources closely related to science and technology. Such resources include domain literature, institutions, scientists, projects, and more. Research limitations: Trial assessment and performance improvement should be carried out for the knowledge service application on the basis of more types of and larger quantities of domain information resources. Practical implications: The domain knowledge service system provides an integrated knowledge discovery tool, as well as several kinds of knowledge mining analysis services for researchers. Originality/value: Our practice can be used as a valuable guide for libraries and information institutions that plan to provide deep domain knowledge services. Purpose: The study was carried out to construct a domain knowledge service system based on the Scientific &amp; Technological Knowledge Organization Systems (STKOS). Design/methodology/approach: The framework of a domain knowledge service system is designed on the basis of the STKOS, and the STKOS science and technology vocabularies, category systems, and ontology networks are applied to realize the knowledge organization and semantic linking of the scientific and technological information resources. Meanwhile, related knowledge-mining analysis algorithms and models are improved, and some tools such as Solr and D3 are used for developing the system. This system integrates various knowledge service modules, including unified search of domain information resources and knowledge-linked navigation, domain hotspot and burst topics monitoring analysis, knowledge structure and evolution analysis, literature citation network, and research agents&#39; cooperative relationship network analysis. Findings: The system can help to refine descriptions, knowledge organization, and the semantic linking of various kinds of information resources closely related to science and technology. Such resources include domain literature, institutions, scientists, projects, and more. Research limitations: Trial assessment and performance improvement should be carried out for the knowledge service application on the basis of more types of and larger quantities of domain information resources. Practical implications: The domain knowledge service system provides an integrated knowledge discovery tool, as well as several kinds of knowledge mining analysis services for researchers. Originality/value: Our practice can be used as a valuable guide for libraries and information institutions that plan to provide deep domain knowledge services.</div
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