[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81483-en":3,"doc-seo-81483-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81483,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Cross-Media Scientific Research Achievements Query Based on Ranking Learning","The paper addresses scientific research achievement information retrieval under the information age, where internet-scale data is dominated by text, images, and other heterogeneous media. Unlike social and news data, research achievements contain many proper nouns and ambiguity, making keyword-only single-mode query inadequate. It develops a framework covering characteristic learning of research results, cross-media querying, ranking learning, and cross-media query systems to support evaluation of project and team output capacity for better decision-making by scientific managers.","Cross-Media Scientific Research Achievements Query Based on  \nRanking Learning  \nBenzhi Wang  \nSchool of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China  \nMeiyu Liang∗ School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China  \nAng Li  \nSchool of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications; Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing, China  \narXiv :2204 . 12 12 1v2 [ cs .IR] 10 Jul 2026  \nAbstract  \nWith the advent of the information age, the scale of data on the Internet is getting larger and larger, and it is full of text, images, videos, and other information. Different from social media data and news data, scientific research achievement information has the characteristics of many proper nouns and strong ambiguity. The traditional single-mode query method based on keywords can no longer meet the needs of scientific researchers and managers of the Ministry of Science and Technology. Scientific research project information and scientific research scholar information contain a large amount of valuable scientific research achievement information. Evaluating the output capability of scientific research projects and scientific research teams can effectively assist managers in decision-making. In view of the above background, this paper expounds on the research status from four aspects: characteristic learning of scientific research results, cross-media research results query, ranking learning of scientific research results, and cross-media scientific research achievement query systems.  \nKeywords  \nscience and technology big data, cross-media retrieval, cross-media semantic association learning, deep language model, semantic similarity  \nThe scale of scientific research results has grown rapidly with the progress of the times and is now enormous. Major universities and research institutions produce scientific research results continuously. These results may come from a professor, a student, or a team, and may be published individually or as part of a scientific research project. Scientific research results include a variety of scientific and technological resource information in different media, including images and text. Efficiently collecting, processing, and storing such multi-source and heterogeneous cross-media scientific research data is an important issue [1]. Deep modularity-based community detection can help identify cohesive collaboration groups in such research networks [2] .  \nWith the advent of the information age, traditional query systems that retrieve scientific research results only through keywords have gradually lagged behind current needs. For researchers, text-result  \n∗ Corresponding author.  \nquery services such as CNKI are relatively complete, but matching models based only on keywords can no longer satisfy daily retrieval needs. Synonymy and polysemy cannot be ignored in text retrieval, and simple keyword matching cannot solve these problems. Deep language models such as BERT provide a foundation for addressing polysemy. Adjacent work on multi-view clustering and uncertainty-aware network estimation further illustrates the challenges of combining heterogeneous representations [3, 4]. Using deep language models to retrain scientific research results for query needs therefore has profound practical and research significance. At the same time, single-modal retrieval will be gradually replaced by cross-modal retrieval. Researchers may want to find relevant papers and patents through a circuit diagram or a neural-network model diagram.  \nFor project managers at universi","cbCaillokwFArxVZ","https://ap.wps.com/l/cbCaillokwFArxVZ","pdf",408360,3,1,5,"English","en",105,"# Abstract\n# Characteristic Learning of Scientific Research Results\n## Cross-media unified feature learning\n## Text and image feature vector construction","[{\"question\":\"How does the proposed work support decision-making for research managers?\",\"answer\":\"By enabling queries that integrate research results, scholars/teams, and projects, the system supports evaluating output capability of projects and teams. 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