K-CAP

We would like to welcome you to the 11th ACM International Conference on Knowledge Capture: K-CAP 2021.

Driven by the increasing demands for knowledge-based applications and the unprecedented availability of information from heterogenous sources, the study of knowledge capture is of crucial importance. Knowledge capture involves the extraction of useful knowledge from vast and diverse data sources as well as its acquisition directly from human experts.

K-CAP 2021 aimed at attracting researchers from diverse areas of Artificial Intelligence, including knowledge acquisition, knowledge representation and reasoning, intelligent user interfaces, problem-solving, planning, agents, information extraction, machine learning, natural language processing, information enrichment and visualization, as well as researchers interested in cyberinfrastructures to foster the publication, retrieval and reuse of data.

Nowadays knowledge is derived from an increasingly diverse set of data resources that differ with regard to their domain, format, quality, coverage, specificity, viewpoint, bias, and most importantly, consumers and producers of the data. The heterogeneity, amount and complexity of the data allow us to answer complex questions that could not be answered in isolation - requiring the interaction of different scientific fields and technologies. A goal of K-CAP is to develop such synergies using systematic and rigorous methodologies.

The call for papers attracted 123 submissions from all over the world, covering a diverse range of topics spanning ontology development, information extraction, knowledge graphs, natural language processing, reasoning, entity linking, querying and knowledge-based applications. From a competitive set of high-quality submissions, we accepted 31 long research papers, 8 short papers, and 2 demo papers.

We encourage everyone to attend the keynote talks that we have planned for K-CAP 2021. The highly anticipated talks by Leila Zia (Wikimedia Foundation) and Ian Horrocks (University of Oxford) will guide us to a better understanding of the future of knowledge graph and knowledge representation technologies, as they become prevalent in real world applications.