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Expert.ai > 实例探究 > Content Enrichment Helps Drive Digital Transformation at ISACA
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Content Enrichment Helps Drive Digital Transformation at ISACA

技术
  • 平台即服务 (PaaS) - 数据管理平台
适用行业
  • 教育
适用功能
  • 产品研发
用例
  • 搜救
服务
  • 数据科学服务
挑战
ISACA, a global community of IT professionals, was looking to enhance the user experience for its members by making it easier for them to search across its repository of over 20,000 documents. The organization wanted to enable users to find information at a granular level, down to the exact paragraph. This required the documents and their subsections to be enriched with semantic tags that could be used for search, accessibility, and content recommendations. The challenge was to develop a comprehensive taxonomy that could automatically classify any kind of document, while avoiding the time and expense of manually creating a taxonomy from scratch.
关于客户
ISACA is a global community of 165,000 IT professionals. The organization provides knowledge, credentials, training, and best practices in auditing, governance, risk, privacy, and security areas. These resources are designed to further organizations’ pursuit of digital trust goals. As part of its digital transformation efforts, ISACA is committed to delivering a world-class experience for its members, who use its services to grow their digital trust capabilities.
解决方案
To meet these challenges, ISACA decided to leverage the expert.ai Platform. This platform would automatically analyze their content and jumpstart the development of a custom taxonomy. The taxonomy would be augmented by the domain expertise of the ISACA team. The new comprehensive taxonomy would automatically classify any kind of document, such as white papers, news and newsletters, journals, frameworks, articles, manuals, etc. The documents would be enriched with semantic tags that could be used to improve how online users access documents through search. This solution would allow ISACA to achieve its goal of enhancing the user experience without the need for manual taxonomy creation.
运营影响
  • Automated the semantic analysis and development of a 700+ node taxonomy
  • Enriched 20,000+ documents with content metadata tags
数量效益
  • Achieved 88% accuracy rate for document classification

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