Centre for Organisational and Social Informatics, Faculty of IT, Monash University, Melbourne, Australia.
JMIR Med Inform. 2014 Apr 23;2(1):e7. doi: 10.2196/medinform.2671.
Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.
This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content
A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.
The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.
The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current.
健康信息门户的持续内容管理是其可持续性和广泛接受的关键特征。在健康领域，领域专家的知识和经验对于内容管理至关重要。在线健康资源的生成速度呈指数级增长，因此，对于领域专家来说，手动检查与特定主题和受众的相关性是一项艰巨的挑战。智能内容发现对于有效的内容管理是一个研究较少的课题。现有的专家认可的内容存储库可以提供必要的优势，自动识别相关资源并评估定性指标。
本文报告了一种针对健康信息门户的智能内容发现和排名自动化技术的设计研究。所提出的技术旨在通过利用现有的专家认可的内容存储库作为支持基础和基准，来评估新内容的适用性，从而提高当前主要依靠人工的门户内容管理流程的效率。
基于对潜在用户的实地研究，建立了内容管理模型。所提出的技术是该内容管理模型的组成部分，并在几个阶段（即查询构建、内容搜索、文本分析和模糊多标准排名）中执行。本文报告的原始贡献包括使用 Wordnet 构建多维搜索查询、使用多词和单词语义作为文本分析的代表性语义，以及使用模糊多标准排名对质量指标进行主观评估。
通过在实际健康信息门户 BCKOnline 门户上进行的实验，检验了所提出技术的可行性。本文呈现了技术生成的中间和最终结果，这些结果有助于确定技术的优势及其对有效内容管理的贡献。
大量在线健康资源的存在是参与健康信息门户和网站内容管理的领域专家面临的关键障碍。所提出的技术已被证明在搜索和识别资源及其相关性方面非常成功。它可以用于支持领域专家进行内容管理，从而确保健康门户是最新的。
