The massive quantity and increasing availability of information on the Web have catalyzed research in a wide range of fields. Today's data-driven world, however, needs more than an abundance of information, and there is a broadening focus on information quality and accuracy. Given the increasing demand for higher quality, accurate information that can be effectively and unambiguously used, the study of precise and scalable knowledge capture is of crucial importance. Knowledge capture involves the extraction of useful knowledge from multiple, potentially heterogeneous sources, as well as its acquisition directly from human experts.
The International Conference on Knowledge Capture, K-CAP, is a forum that brings together members of disparate research communities who are interested in efficiently and precisely capturing knowledge from a variety of heterogeneous sources, and in creating representations that can be useful for automated reasoning, analysis, and other forms of machine processing, as well as to support users in knowledge-intensive collaborative tasks.
To discuss and advance the goals of scalable and precise knowledge capture, K-CAP 2021 aims to attract researchers from diverse areas of artificial intelligence, including knowledge representation and reasoning, knowledge acquisition, semantic web, intelligent user interfaces for knowledge acquisition and retrieval, query processing and question answering over heterogeneous knowledge bases, novel evaluation paradigms, problem-solving and reasoning, agents, information extraction from structured or unstructured data, 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.
The eleventh International Conference on Knowledge Capture, K-CAP 2021, features a full papers track for research papers, as well as tracks for short papers, for visionary ideas, and for demos.
Authors of all paper types will present their work in plenary sessions during the conference. Presentation timing for the different types of papers will be announced once the program is crafted.
K-CAP is not a double-blind conference, hence authors should list their names and affiliations on the submission. Please use the ACM 2 column SIG Conference Proceedings template for your submission. Submissions in HTML format are welcome, so long as authors of HTML submissions also provide a conversion of their submission to a PDF file that adheres to the required ACM template for proceedings. Papers submitted to the main conference track should not have been published before in an archival venue, or currently be under review in an archival venue. Authors are welcome to submit papers that have been published as a preprint on arXiv, or in a non-archival venue like a workshop.
All submissions to K-CAP should be made through EasyChair: https://easychair.org/conferences/?conf=kcap21
This year we will embrace the FAIR principles by collecting structured metadata of the datasets, software, ontologies and methods generated by K-CAP submissions. Authors will be encouraged to add these resources in the EasyChair submission form (together with a brief description). Accepted papers with resources will be highlighted in the main K-CAP conference page.
As a published ACM author, you and your co-authors are subject to all ACM Publications Policies, including ACM's new Publications Policy on Research Involving Human Participants and Subjects