K-CAP: International Conference on Knowledge Capture



Past:   Proceedings on DBLP

Future:  Post a CFP for 2024 or later   |   Invite the Organizers Email


All CFPs on WikiCFP

Event When Where Deadline
K-CAP 2023 12th International Conference on Knowledge Capture
Dec 5, 2023 - Dec 7, 2023 Pensacola, Florida, USA Aug 27, 2023
K-CAP 2017 The Ninth International Conference on Knowledge Capture
Dec 4, 2017 - Dec 6, 2017 Austin, Texas, USA Jul 28, 2017
K-CAP 2013 Knowledge Capture Conference
Jun 23, 2013 - Jun 26, 2013 Banff, Canada Feb 8, 2013
K-CAP 2011 Knowledge Capture Conference
Jun 26, 2011 - Jun 29, 2011 Banff, AB, Canada TBD
K-CAP 2009 The Fifth International Conference on Knowledge Capture
Sep 2, 2009 - Sep 5, 2009 Redondo Beach, CA, USA Apr 15, 2009
K-CAP 2007 International Conference on Knowledge Capture 2007
Oct 28, 2007 - Oct 31, 2007 Whistler, Canada TBD

Present CFP : 2023

Knowledge has played a fundamental role since the inception of artificial intelligence. While the forms in which algorithms have leveraged knowledge have evolved over time, the need for efficient representations is ever more critical. Indeed, recent advances of AI, such as the stunning performance of large language models have relied on the large amount of data available on the Web. There is growing agreement among researchers that it's important to look beyond the sheer volume of data, and instead also prioritize the development of methods that are accurate, precise, and efficient for capturing knowledge.

The International Conference on Knowledge Capture, K-CAP, aims at bringing together an interdisciplinary group of researchers on a diverse set of topics with interest in the development of knowledge capture. This involves the design and development of formalisms, methods and tools that enable efficient and precise extraction and organization of knowledge from different sources and for different modalities of use including, for example, automated reasoning, machine learning and human-machine teaming.

To enable a vibrant and constructive discussion on scalable and precise knowledge capture, K-CAP 2023, calls for the participation of researchers from diverse areas of artificial intelligence, including, but not limited to, 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, ethics and AI, explainability, neurosymbolic AI, 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.

K-CAP is an in-person conference, and remote participation will not be supported.

Topics of interest

Areas of interest for submissions to K-CAP 23 include, but are not limited to, the following topics:

Knowledge representation
Knowledge acquisition
Ethical aspects related to knowledge capture and acquisition
Knowledge capture for supporting explainability and, vice-versa, leveraging explainability approaches for knowledge capture
Intelligent user interfaces for knowledge acquisition and retrieval
Innovative query processing and question answering over heterogeneous knowledge bases
Novel evaluation paradigms for knowledge capture
Problem-solving and reasoning
The role of knowledge and knowledge capture in neuro-symbolic AI
Compact knowledge representation such as constraint networks and graphical models
Knowledge capture in multi-agent systems
Intersection of planning and knowledge capture
Information extraction from text
The role of metadata in knowledge capture processes
Multi-modal knowledge capture from text, tables, images, video or sound
Machine learning and representation learning
Information enrichment and visualization
The role of language models in knowledge graph construction and representation
Techniques for extracting structured knowledge from large-scale language models
Applications of deep learning to knowledge representation and reasoning, such as graph neural networks and graph convolutional networks
Advancements in representation learning and deep learning for knowledge capture
Utilizing deep learning for information extraction from structured and unstructured data to improve knowledge capture


The Twelfth International Conference on Knowledge Capture, K-CAP 2023, features a full papers track for research papers, as well as tracks for short papers, for visionary ideas.

Full papers, which describe original research, can be up to 8 pages long including references and appendices. Full papers will appear in the conference proceedings and will be citable as K-CAP 2023 publications.
Short papers, which may describe (possibly preliminary or open-ended) research, applications (academic, industrial or otherwise), and late breaking results, can be up to 4 pages long, including references and appendices. Short papers will also appear in the conference proceedings and will be citable as K-CAP 2023 publications.
Visionary papers, which describe outrageous ideas or potentially preliminary, but highly innovative research, can be up to 2 pages long, including references and appendices. Visionary papers will be presented as short talks in the conference program.

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. Authors are allowed to use generative models as a tool but not to include them as co-authors. The use of such tools must be acknowledged and properly documented in the papers. In particular, authors are encouraged to add a credit statement capturing the contribution role of authors and models.

All submissions to K-CAP should be made through EasyChair: https://easychair.org/conferences/?conf=kcap2023.

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.

An author should register per accepted SHORT or FULL paper in order to appear in the proceeding.

Related Resources

KR 2024   Principles of Knowledge Representation and Reasoning
IDC 2024   Intelligent Distributed Computing
ECML-PKDD 2024   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
SCIDOCA 2024   Eighth International Workshop on SCIentific DOCument Analysis
DMKD 2024   2024 International Conference on Data Mining and Knowledge Discovery(DMKD 2024)
AKR3 2024   AKR3: 1st International Workshop on Actionable Knowledge Representation for Robots (at ESWC)
AIKE 2024   7th IEEE International Conference on Artificial Intelligence and Knowledge Engineering
SESBC 2024   5th International Conference on Software Engineering, Security and Blockchain
CONCEPTS 2024   1st International Joint Conference on Conceptual Knowledge Structures
DS 2024   Discovery Science 2024