The 2016 Pacific Rim Knowledge Acquisition Workshop (PKAW 2016)
Welcome to the 2016 Pacific Rim Knowledge Acquisition Workshop (PKAW 2016) to be held at Phuket, Thailand on August 22-23, 2016. Over the past two decades, PKAW has provided a forum for researchers and practitioners working in the area of knowledge acquisition and machine intelligence. In 2016, PKAW 2016 will be held as part of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016). The purpose of this workshop is to provide a chance for intensive discussion on the aspects of knowledge acquisition.
In recent years, unprecedented data, called big data, has become available and knowledge acquisition and learning from big data is increasing in importance. Various knowledge can be acquired not only from human experts but also from diverse data. Simultaneous acquisition from both data and human experts increases its importance. Multidisciplinary research including knowledge engineering, machine learning, natural language processing, human computer interaction, and artificial intelligence are required. We invite authors to submit papers on all aspects of these area.
Another important and related area is applications. Not only in the engineering field but also in the social science field (e.g., economics, social networks, and sociology), recent progress of knowledge acquisition and data engineering techniques is realizing interesting applications. We also invite submissions that present applications tested and deployed in real-life settings. Those papers should address lessons learned from application development and deployment.
All papers will be peer reviewed, and those accepted for the workshop will be included in the Springer LNAI proceedings.
TOPICS OF INTEREST
Papers are invited for consideration in all aspects of knowledge acquisition, engineering and management for intelligent systems, including (but not restricted to):
- 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
- Languages and frameworks for knowledge and knowledge modelling information systems or decision support systems
- Methods and techniques for sharing and reusing knowledge
- Ontology and its role in knowledge acquisition
- Mining the Semantic Web, the Linked Data and the Web of Data
- Hybrid approaches combining knowledge engineering and machine learning
- Innovative user interfaces
- Big data capture, representation and analytics
- Crowd-sourcing for data generation and problem solving
- Software engineering and knowledge engineering
- Algorithms, tools and techniques for machine intelligence
- Knowledge acquisition applications tested and deployed in in real-life settings
Paper format and length
Papers must follow the Springer Lecture Notes format. The paper should be between 10 to 15 pages long. For format templates, please see the Springer.
Submission page on Easychair.
|Notification of Acceptance||May.09 2016|
|Early Registration Deadline||May.23 2016|
|Camera Ready Due||May.25 2016|
|Workshop Date||Aug.22,23 2016|
Prof. Paul Compton, University of New South Wales, Australia
Prof. Hiroshi Motoda, Osaka University, Japan
Prof. Hayato Ohwada, Tokyo University of Science,Japan
Prof. Kenichi Yoshida, University of Tsukuba, Japan
A/Prof. Byeong Ho Kang, University of Tasmania, Australia
Prof. Deborah Richards, Macquarie University, Australia
Caren Han, University of Tasmania, Australia