Retrieval of Scientific and Technological Resources for Experts and Scholars

• URL: http://arxiv.org/abs/2204.06142v1
• Date: Wed, 13 Apr 2022 02:32:09 GMT
• Title: Retrieval of Scientific and Technological Resources for Experts and Scholars
• Authors: Suyu Ouyang and Yingxia Shao and Ang Li
• Abstract summary: The scientific and technological resources of experts and scholars are mainly composed of basic attributes and scientific research achievements. Due to information asymmetry and other reasons, the scientific and technological resources of experts and scholars cannot be connected with the society in a timely manner. This paper sorts out the related research work in this field from four aspects: text relation extraction, text knowledge representation learning, text vector retrieval and visualization system.
• Score: 20.89926457148302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Institutions of higher learning, research institutes and other scientific research units have abundant scientific and technological resources of experts and scholars, and these talents with great scientific and technological innovation ability are an important force to promote industrial upgrading. The scientific and technological resources of experts and scholars are mainly composed of basic attributes and scientific research achievements. The basic attributes include information such as research interests, institutions, and educational work experience. However, due to information asymmetry and other reasons, the scientific and technological resources of experts and scholars cannot be connected with the society in a timely manner, and social needs cannot be accurately matched with experts and scholars. Therefore, it is very necessary to build an expert and scholar information database and provide relevant expert and scholar retrieval services. This paper sorts out the related research work in this field from four aspects: text relation extraction, text knowledge representation learning, text vector retrieval and visualization system.

Related papers

• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is a large language model-powered research idea writing agent. It generates problems, methods, and experiment designs while iteratively refining them based on scientific literature. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
  This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
  arXiv  Detail & Related papers  (2024-04-09T15:01:51Z)
• Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities [11.35513523308132]
  We discuss developments and advances in the use of graph-based technologies in life sciences. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations.
  arXiv  Detail & Related papers  (2023-09-29T14:03:34Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• KnowledgeShovel: An AI-in-the-Loop Document Annotation System for Scientific Knowledge Base Construction [46.56643271476249]
  KnowledgeShovel is an Al-in-the-Loop document annotation system for researchers to construct scientific knowledge bases. The design of KnowledgeShovel introduces a multi-step multi-modalAI collaboration pipeline to improve data accuracy while reducing the human burden. A follow-up user evaluation with 7 geoscience researchers shows that KnowledgeShovel can enable efficient construction of scientific knowledge bases with satisfactory accuracy.
  arXiv  Detail & Related papers  (2022-10-06T11:38:18Z)
• Artificial Intelligence in Concrete Materials: A Scientometric View [77.34726150561087]
  This chapter aims to uncover the main research interests and knowledge structure of the existing literature on AI for concrete materials. To begin with, a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science. Scientometric tools such as keyword co-occurrence analysis and documentation co-citation analysis were adopted to quantify features and characteristics of the research field.
  arXiv  Detail & Related papers  (2022-09-17T18:24:56Z)
• Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences [14.130765322587264]
  In recent years, with the continuous progress of science and technology, the number of scientific research achievements is increasing day by day. The convening of scientific and technological academic conferences will bring large number of academic papers, researchers, research institutions and other data. It is of great significance to use deep learning technology to mine the core information in the data of scientific and technological academic conferences.
  arXiv  Detail & Related papers  (2022-04-11T06:15:45Z)
• Accurate Portraits of Scientific Resources and Knowledge Service Components [18.014902048632912]
  The main body of scientific and technological resources is composed of academic-style resources or entities such as papers, patents, authors, and research institutions. There is a rich relationship network between resources, from which a large amount of cutting-edge scientific and technological information can be mined. How to construct a complete and accurate representation of scientific and technological resources is an urgent problem.
  arXiv  Detail & Related papers  (2022-04-11T06:03:29Z)
• CitationIE: Leveraging the Citation Graph for Scientific Information Extraction [89.33938657493765]
  We use the citation graph of referential links between citing and cited papers. We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
  arXiv  Detail & Related papers  (2021-06-03T03:00:12Z)
• Technological impact of biomedical research: the role of basicness and novelty [2.741266294612776]
  Using a corpus of 3.8 million papers published between 1980 and 1999, we find that basic science papers and novel papers are substantially more likely to achieve direct technological impact. Further analysis that limits to papers with technological impact reveals that basic science and novel science have more patent citations, experience shorter time lag, and have impact in broader technological fields.
  arXiv  Detail & Related papers  (2020-06-03T18:31:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.