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PKAW 2026: Principle and practice of data and Knowledge Acquisition Workshop

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截稿日期:
2026-07-15
通知日期:
2026-09-15
会议日期:
2026-11-17
会议地点:
Guangzhou, China
届数:
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ICORE: C   浏览: 8890   关注: 0   参加: 0
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征稿

PKAW 2026 (Principle and practice of data and Knowledge Acquisition Workshop) is a ICORE C conference held in Guangzhou, China on 2026-11-17. The paper submission deadline is 2026-07-15. Acceptance notifications are sent on 2026-09-15.

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 2026 will be held at the 23rd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2026) in Guangzhou, China. A wide range of topics related to knowledge acquisition and representation are greatly welcome. Topics of Interest All aspects of AI, machine learning, 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
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