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

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.