The submission deadline has been extended to 01 August 2024!!!

Welcome to the 2024 Principle and practice of data and Knowledge Acquisition Workshop (PKAW). In the past, the workshops have been held in Guilin (2006), Hanoi (2008), Daegu (2010), Kuching (2012), Gold Coast (2014), Phuket (2016), Nanjing (2018), Fiji (2019), Yokohama (2020, online), Shanghai (2022, hybrid), and Jakarta (2023, hybrid). PKAW 2024 will be collocated with the 21st Pacific Rim International Conference on Artificial Intelligence (PRICAI 2024) and held in Kyoto, Japan in November 2024.

PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-art in the areas of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW 2024 will continue the above focus and welcome the contributions to the multi-disciplinary approach of human and big data-driven knowledge acquisition and AI techniques and applications. Wide range of topics related to knowledge acquisition and representation are greatly welcome.

Website: https://pkawwebsite.github.io/2024/

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Important Dates
• Paper Submission: 01 August 2024 (UTC -12)
• Notification: 15 September 2024
• Camera-Ready Submission: 22 September 2024
• Workshop Date: 18-19 November 2024


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Areas of Interest
All aspects of knowledge acquisition, data engineering and management for intelligent systems, including (but not restricted to):
• Knowledge Acquisition
o Fundamental views on knowledge that affect the knowledge acquisition process and the use of knowledge in knowledge engineering
o Algorithmic approaches to knowledge acquisition
o Tools and techniques for knowledge acquisition, knowledge maintenance and knowledge validation
o Evaluation of knowledge acquisition techniques, tools and methods
o Ontology and its role in knowledge acquisition
o Knowledge acquisition applications tested and deployed in real-life settings
o Knowledge processing for generative AI
• Knowledge Representation and Discovering
o Knowledge representation learning
o Temporal knowledge graph
o Data linkage
o Data analytics and mining
o Big data acquisition and analysis
o Machine learning/deep learning
o Semantic Web, the Linked Data and the Web of Data
• Responsible Data/Knowledge Management and System
o Transparency, explainability, trust, and accountability
o Privacy and security
o Other ethical concerns
• Knowledge-aware Application
o Question answering
o Recommendation system
o Domain-related application
• Human-centric Knowledge Engineering
o Human-machine collaboration, integration, interaction, delegation, dialog
o Hybrid approaches combining knowledge engineering and machine learning
• Other Topics
o Experience and Lesson learned
o Reproducibility and negative results of knowledge engineering
o Innovative user interfaces
o Crowd-sourcing for data generation and problem solving


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Paper Submission
PKAW will not accept any paper that, at the time of submission, is under review in, or 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 2024 will adopt a 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.

Proceedings of PKAW 2024 will be published by Springer as a volume of Lecture Notes in Artificial Intelligence (LNAI) series. 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 not exceed 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.


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Submission link: https://cmt3.research.microsoft.com/PKAW2024