This paper describes the Common Pattern Specification Language (CPSL) that was developed during the TIPSTER program by a committee of researchers from the TIPSTER research sites. Many information extraction systems work by matching regular expressions over the lexical features of input symbols. CPSL was designed as a language for specifying such finite-state grammars for the purpose of specifying information extraction rules in a relatively system-independent way. The adoption of such a common language would enable the creation of shareable resources for the development of rule-based information extraction systems.

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract finegrained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports 1 , resources, and shared services are publicly available 2 .

This paper describes the Common Pattern Specification Language (CPSL) that was developed during the TIPSTER program by a committee of researchers from the TIPSTER research sites. Many information extraction systems work by matching regular expressions over the lexical features of input symbols. CPSL was designed as a language for specifying such finite-state grammars for the purpose of specifying information extraction rules in a relatively system-independent way. The adoption of such a common language would enable the creation of shareable resources for the development of rule-based information extraction systems.

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract finegrained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures and knowledge subgraphs as evidence. All of the data, KGs, reports 1 , resources and shared services are publicly available 2 .

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