Topics of Interests

Researchers in the biomedical community have access to a tremendous amount of information (e.g. MEDLINE, the major database for biomedical research, has more than 26 million references to journal articles), which is a mixed blessing. On the one hand, existing biomedical data resources can lead to information overload,i.e., it is hardly possible for researchers to keep pace with available biomedical information on the Web or in biomedical knowledge bases. On the other hand, the huge amount of biomedical information can act as valuable sources for knowledge discovery and learning algorithms. This workshop will explore both sides of the coin: we are interested in exploring effective solutions for searching over biomedical literature based on the knowledge extracted from biomedical sources. Effective search helps researchers explore the literature more conveniently, and hence helps to reduce the impact of information overload. In addition, knowledge-based search systems need mechanisms for structured, semantic-rich representation and effective use of biomedical knowledge learnt from the available data.

With the emergence of unprecedented volumes of biomedical publications as a result of the COVID-19 pandemic, many of which appear in pre-publication research repositories such as medRxiv and bioRxiv, this workshop will solicit work that address timely and pressing research challenges at the intersection of Knowledge Representation and Semantics for the purpose of searching biomedical literature. We are also interested in semantic-based retrieval techniques that enhance biomedical knowledge synthesis and systematic literature review. The topics of interest to the workshop include, but are not limited to:

  • knowledge representation and semantics for biomedical literature retrieval
  • Knowledge graphs in biomedical search
  • Biomedical knowledge source integration
  • Biomedical knowledge graph construction and embedding
  • Biomedical ontologies in search
  • Semantic features in biomedical literature classification and ranking
  • Learning to rank biomedical literature
  • Semantics and Search for biomedical knowledge synthesis and systematic literature review
  • Entity linking and semantic annotation in biomedical texts
  • Datasets, metrics, and evaluation
  • Knowledge discovery / synthesis from literature
  • Biomedical literature retrieval for the systematic review process
  • Search and semantics of the publication process (reviewing,quality assurance, discovering (self ) plagiarism)
  • Knowledge integration from multilingual Biomedical literature
  • Biomedical literature datasets, such as CORD-19 and evaluation methods
  • Search and semantic tools for knowledge synthesis, and systematic reviews