Due to the exponential growth in the number of scientific articles published in the biomedical domain, obtaining the most relevant articles to a topic of interest, integrating different parts of knowledge from various studies, and finding reliable and scientifically sound studies present significant challenges. While traditional term-based information retrieval and machine learning techniques can be employed for literature search, ranking and integration, such approaches lack an effective mechanism for retrieving scientific articles that usually contain domain-specific terminology, phrases,and abbreviations, where text can have differing and ambiguous semantics based on the given context and domain. Knowledge representation and Semantics-enabled techniques have already shown the potential to systematically curate, organize, retrieve and interpret content in ways that relates well to human understanding.
The recent major collaborative effort for rapid and effective retrieval of literature around COVID-19 is a strong indication for the need to develop effective retrieval techniques specifically tailored for the biomedical domain. The White House Office of Science and Technology Policy along with major institutes such as Chan Zuckerberg Initiative, Microsoft Research, the Allen Institute for Artificial Intelligence, and the National Institutes of Health’s National Library of Medicine, among others, shared the COVID-19 Open Research Dataset, known as CORD-19, which consists of 30,000 scientific articles about the virus known as SARS-CoV-21. One of the objectives behind the release of such a dataset is for researchers to build tools and techniques that can identify and effectively retrieve information from the literature.
To this end, the main objective of this workshop is to bring together researchers from Web search, semantic Web, and biomedical communities, to present and discuss the latest methods and results in biomedical information and knowledge representation, integration, and retrieval. Given the fact that searching and retrieving all studies that address a research question is one of the initial and main important tasks in devising a systematic review, we also aim at investigating the application of semantics and Web search in creating biomedical systematic literature review. The distinguishing feature of this workshop is its focus on leveraging semantic-techniques for information retrieval from biomedical literature. This further means that it puts rigorous attention on semantic-based techniques that allow for the creation, analysis, and integration of biomedical knowledge bases, with the ultimate objective of employing such knowledge bases to improve search performance over biomedical literature.