PKAW2022: Principle and practice of data and Knowledge Acquisition Workshop Shanghai, China, November 10-13, 2022 |
Conference website | https://pkawwebsite.github.io/2022 |
Submission link | https://easychair.org/conferences/?conf=pkaw2022 |
Submission deadline | August 7, 2022 |
PKAW (Principle and Practice of Data and Knowledge Acquisition Workshop) was established in 1980s as an integral part of PRICAI (Pacific Rim International Conference on Artificial Intelligence). PKAW 2022 will be held as a workshop at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022) in Shanghai, China. A wide range of topics related to knowledge acquisition and representation are greatly welcome.
Submission Guidelines
PKAW will not accept any paper that, at the time of submission, is under review for, has already been published in, or has already been accepted for publication in, a journal or another venue with formally published proceedings. If part of the work has been previously published, authors are strongly encouraged to cite and compare/contrast the new contributions with the parts that were already published before. The paper must substantially extend the previously published work.
PKAW 2022 will adopt single-blind rule for the reviewing process, i.e., the authors do not know the names of the reviewers, but the reviewers can infer the names of the authors from the submission.
All papers for the review should be submitted electronically using the conference management tool in PDF format and formatted using the Springer LNAI template. The paper should be 12 pages long (excluding references). For accepted papers, the latex source files and a camera-ready version are required to be submitted using the Springer LNAI template.
Paper submission link: https://easychair.org/conferences/?conf=pkaw2022
Page limit:
Full paper: 12 pages
Short paper: 8 pages
List of Topics
All aspects of knowledge acquisition, data engineering and management for intelligent systems, including (but not restricted to):
- Knowledge Acquisition
- Fundamental views on knowledge that affect the knowledge acquisition process and the use of knowledge in knowledge engineering
- Algorithmic approaches to knowledge acquisition
- Tools and techniques for knowledge acquisition, knowledge maintenance and knowledge validation
- Evaluation of knowledge acquisition techniques, tools and methods.
- Ontology and its role in knowledge acquisition
- Knowledge acquisition applications tested and deployed in real-life settings
- Knowledge Representation and Discovering
- Knowledge representation learning
- Temporal knowledge graph
- Data linkage
- Data analytics and mining
- Big data acquisition and analysis
- Machine learning/deep learning
- Semantic Web, the Linked Data and the Web of Data
- Responsible Data/Knowledge Management and System
- Transparency, explainability, trust, and accountability
- Privacy and security
- Other ethical concerns
- Knowledge-aware Application
- Question answering
- Recommendation system
- Domain-related application
- Human-centric Knowledge Engineering
- Human-machine collaboration, integration, interaction, delegation, dialog
- Hybrid approaches combining knowledge engineering and machine learning
- Other Topics
- Experience and Lesson learned
- Reproducibility and negative results of knowledge engineering
- Innovative user interfaces
- Crowd-sourcing for data generation and problem solving
Publication
All papers will be peer-reviewed, and those accepted for the workshop will be included in the Arxiv conference proceedings. Selected papers will be also invited to submit their extension to a special issue in the Human-Centric Intelligent Systems journal.
Human-Centric Intelligent Systems link: https://www.springer.com/journal/44230
Contact
For any question, please send your email to Dr. Qing Liu (q.liu@data61.csiro.au) and Dr. Wenli Yang (yang.wenli@utas.edu.au).