The world has become increasingly data-driven and reliant on semi-automatic technologies. Effective access to, and use of, information is a key enabler for progress. Given this need and the unprecedented availability of information on the Web, the study of knowledge capture is of crucial importance. Knowledge capture involves the extraction of useful knowledge from vast and diverse online sources as well as its acquisition directly from human experts.
To achieve the ambitious goals of scalable, robust knowledge capture, the Tenth International Conference on Knowledge Capture aims at attracting researchers from diverse areas of Artificial Intelligence, including knowledge representation, knowledge acquisition, Semantic and World Wide Web, intelligent user interfaces for knowledge acquisition and retrieval, innovative query processing and question answering over heterogeneous knowledge bases, novel evaluation paradigms, problem-solving and reasoning, planning, agents, information extraction from text, metadata, tables and other heterogeneous data such as images and videos, machine learning and representation learning, information enrichment and visualization, as well as researchers interested in cyber-infrastructures to foster the publication, retrieval, reuse, and integration of data.
Today these data come from an increasingly heterogeneous and set of multi-modal resources that differ with regards to their domain, media format, quality, coverage, viewpoint, bias, and most importantly, consumers and producers of the data. Some kinds of data are produced by purely automatic methods and are structured, while others (like DBpedia) draw at least in part on multiple technologies and stakeholders coming together. More than the sheer amount of these data, their heterogeneity allows us to arrive at better models and answer complex questions that cannot be addressed in isolation but require the interaction of different scientific fields or perspectives. A goal of the conference is to develop such synergies using systematic and rigorous methodologies. Additionally, we recognize that previous modes and metrics of evaluation may not always be sufficient for assessing empirical progress at such complex levels.