CRII: III: A Socio-technical Approach for Biomedical Content Authoring and Structured Web Publishing

从世界上无数的可用信息中有效地检索上下文适当的，准确的和有价值的信息是任何成功的搜索引擎的基石。例如，在图书馆里，图书管理员采用复杂的策略，从许多可用的书名中为读者检索适当的书籍，并取得不同程度的成功。类似地，大量的数字生物医学文本内容，诸如研究论文、临床笔记、生物医学报告等，近年来，由于生物医学研究和临床实践的空前增长，获取这些生物医学内容的有效策略对于将正确的信息从科学研究界及时传递给同行调查人员和感兴趣的个人至关重要。即使有价值的信息嵌入在线生物医学内容，它仍然是不透明的信息检索和知识提取搜索引擎。这主要是因为大多数生物医学内容都是非结构化的，只有少量或没有明确的机器可解释的语义（上下文感知）标记或注释。例如，Google搜索引擎算法要求元数据以上下文感知（语义）方式正确标记生物医学内容，从而实现更精确的搜索。因此，开发一种适当的技术基础设施，使内容用户能够向生物医学内容添加上下文感知标记（语义注释），然后共享它们，以提高准确性，从而提高其可访问性，这将是一个改变游戏规则的因素，也是时代的需要。该项目通过开发最先进的可免费访问的交互式系统，使生物医学领域不同专业水平的个人能够在没有任何技术背景的情况下合作创作和发布生物医学语义内容，从而促进科学和技术的进步。因此，这项研究民主化的过程中，目前提供给精英用户的语义内容创作。该项目旨在让社会经济多样化的学习者参与实践经验，实现同行知识共享，并开辟许多职业发展机会。这项研究不仅将产生公共教育材料，书目和可重复使用的经验数据，以支持广泛的生物医学利益相关者，但它也将提供机会，通过整合研究成果，在生物医学信息学课程课程的课程增强。本研究引入了一个开箱即用的社会技术方法和一套新的语义内容模型，开发开放的交互式系统，克服研究的挑战，平衡的速度和准确性和语义保存语义内容创作和结构化发布。平衡速度和准确性是关键的研究挑战;在内容创作期间实时找到正确的语义注释是非常困难的，因为通常一个语义注释在具有不同文本或内涵的多个生物医学本体中可用。本研究开发了一个生物医学语义内容创作系统，通过在整个过程中保持原作者在循环中，以平衡可用的生物医学注释的速度和准确性。智能算法，如AuthorsLikeMe和RecommendMe的开发是为了从其他从事类似生物医学内容的作者那里获得实时帮助。咖啡馆：虚拟协作环境串联使新手作者寻求专家的帮助，其中专家基于帮助他人的激励模型而被吸引。保持内容级语义仍然是在线出版的关键挑战和关注中心。该团队通过开发新的语义模型来解决这个问题，扩展了研究引擎认可的受控元数据，以在网络上发布生物医学内容。多个用户友好的，可定制的界面开发的创作，发布，概念搜索，知识共享与同行。这项研究的副产品之一将是一种独特的生物医学注释语料库，它将通过API公开提供给任何潜在的用途。通过用户研究和系统评估，从用户的角度了解系统的效率和内容质量。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Efficient retrieval of context-appropriate, accurate and valuable information from the myriad of information available in the world is the bedrock of any successful search engine. For instance, in a library, librarians employ complex strategies with varying degrees of success to retrieve the appropriate book for a reader out of the many available titles. Similarly, a large volume of digital biomedical textual content such as research papers, clinical notes, biomedical reports, etc., has been produced in recent years due to the unprecedented growth of biomedical research and clinical practices. Efficient strategies for accessing these biomedical contents are crucial for allowing a timely transfer of correct information from the scientific research community to peer investigators and interested individuals. Even though valuable information is embedded in online biomedical contents, it remains opaque to information retrieval and knowledge extraction search engines. It is mainly because most biomedical contents are unstructured with minor or no explicit machine-interpretable semantic (context-aware) markups or annotations. For example, Google search engine algorithms require metadata to properly tag biomedical contents in a context-aware (semantic) fashion enabling more precise searches. Thus, developing an apt technology infrastructure to empower content users to add context-aware tagging (semantic annotations) to the biomedical content and later share them to promote their accessibility with improved accuracy would be a game-changer and the need of the hour. This project promotes the progress of science and technology by developing state-of-the-art freely accessible interactive systems that enable individuals at different expertise levels in the biomedical domain without any technical background to collaboratively author and publish biomedical semantic content. Hence, this research democratizes the process of semantic content authoring currently available to elite users. This project intends to involve a socio-economically diverse pool of learners in getting hands-on experience, enabling peer-to-peer knowledge sharing and opening up many career progression opportunities. The research will not only generate public educational material, bibliographies, and reusable empirical data to support wide-ranging biomedical stakeholders, but it will also provide opportunities for curriculum enhancement by integrating research results in biomedical informatics courses. This research introduces an out-of-the-box socio-technical approach and a novel set of semantic content models to develop open interactive systems overcoming the research challenges of balancing speed and accuracy and semantic preservation during semantic content authoring and structured publishing. Balancing speed and accuracy is the key research challenge; finding the right semantic annotations in real-time during content authoring is extremely difficult since often one semantic annotation is available in multiple biomedical ontologies with different text or connotations. This research develops a biomedical semantic content authoring system to balance the speed and accuracy of available biomedical annotators by keeping the original author in the loop during the entire process. Intelligent algorithms such as AuthorsLikeMe and RecommendMe are developed to get real-time help from other fellow authors working on similar biomedical contents. Semantically Cafe: A virtual collaboration environment in tandem enables novice authors to seek help from experts where the experts are attracted based on an incentive model for helping others. Preserving content-level semantics remains the key challenge and center of attention of online publishing. The team addresses this by developing novel semantic models extending the research engines endorsed controlled metadata to publish the biomedical contents on the web. Multiple user-friendly, customizable interfaces are developed for authoring, publishing, concept searching, and knowledge sharing with peers. One of the by-products of this research will be one-of-its-own kind of biomedical annotations corpus that will be openly available through the API for any potential usage. Understanding the system's efficiency and the quality of the content from the users' perspective will be studied through user studies and system evaluations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.