Optimal information and knowledge management is crucial for organizations to
achieve their objectives efficiently. As a rare and valuable resource,
effective knowledge management provides a strategic advantage and has become a
key determinant of organizational success. The study aims to identify critical
success and failure factors for implementing knowledge management systems in
government organizations. This research employs a descriptive survey
methodology, collecting data through random interviews and questionnaires. The
study highlights the critical success factors for knowledge management systems
in government organizations, including cooperation, an open atmosphere, staff
training, creativity and innovation, removal of organizational constraints,
reward policies, role modeling, and focus. Conversely, failure to consider
formality, staff participation, collaboration technologies, network and
hardware infrastructure, complexity, IT staff, and trust can pose significant
obstacles to successful implementation.

本研究旨在识别政府机构实施知识管理系统的关键成功和失败因素，结果表明，合作、开放的氛围、员工培训、创新、组织约束的消除、奖励政策、角色建模和专注是政府机构知识管理系统的关键成功因素，而忽视正式性、员工参与、协作技术、网络和硬件基础设施、复杂性、IT 员工和信任等因素则可能对成功实施构成重大障碍。

政府组织中知识管理实施成功的导航：一种二型模糊方法

Navigating Knowledge Management Implementation Success in Government  Organizations: A type-2 fuzzy approach

Information extraction from scholarly articles is a challenging task due to
the sizable document length and implicit information hidden in text, figures,
and citations. Scholarly information extraction has various applications in
exploration, archival, and curation services for digital libraries and
knowledge management systems. We present MORTY, an information extraction
technique that creates structured summaries of text from scholarly articles.
Our approach condenses the article's full-text to property-value pairs as a
segmented text snippet called structured summary. We also present a sizable
scholarly dataset combining structured summaries retrieved from a scholarly
knowledge graph and corresponding publicly available scientific articles, which
we openly publish as a resource for the research community. Our results show
that structured summarization is a suitable approach for targeted information
extraction that complements other commonly used methods such as question
answering and named entity recognition.

本文介绍 MORTY 信息提取技术，将学术论文全文压缩为以属性 - 值对为结构的文本片段，适用于数字图书馆和知识管理系统的探索、档案和策划服务，同时通过学术知识图谱和公开的科学文章，提供了一个大规模的学术数据集，研究结果表明，结构化摘要是补充其他常用方法（如问答和实体识别）的一种合适的信息提取方法。