(Tentative) Meeting Agenda

The agenda on this page presents the general themes for the two days. The agenda is subject to change, up to and during the workshop. The workshop will be focused around break-out groups and interactive discussions.

Day 1 (application-driven challenges and opportunities):

  1. Big-data science: How can heterogeneous knowledge from diverse sources be integrated to produce novel scientific insights? What are the unique methods that KR brings to data analysis and integration? What are the use cases for KR in specific scientific disciplines? What KR advances are required to realize these use cases? 
  2. Linked and open data on the semantic web: What is the structure of the linked open data (or lack of it)? What are use cases, based on the types of linked data available today, of applications that use this data to provide new useful knowledge? What novel KR methods are required to provide useful knowledge from the heterogeneous and often messy linked data? 
  3. AI Applications: What KR work is critical for the AI applications in Machine Learning, Computer Vision, Natural Language Processing, and Robotics? How should this knowledge be collected and represented? How will this knowledge be used? What KR technologies need to advance to enable these applications?
Day 2 (KR approaches and themes): 

  1. Representing common-sense knowledge and uncertainty: What opportunities for commonsense knowledge and reasoning are presented by the social web and mobile technologies? What opportunities does uncertainty modeling and machine learning afford to commonsense knowledge acquisition and reasoning? What opportunities does computer vision and natural-language processing enable for commonsense knowledge and reasoning? 
  2. Scalability of knowledge representation: What are the new opportunities presented by increased availability of powerful distributed computing environment, such as the web and cloud computing? What are the novel KR infrastructures that such environments enable? What are the challenges of the distributed knowledge representation? How do we address the inconsistency of the knowledge that might come form different sources? How do we ensure that reasoning across the distributed infrastructure preserves the required levels of secrecy and privacy? 
  3. KR, people, and the social networks: How do we represent and reason about individual preferences beliefs on the scale of today’s social networks? How do we represent trust and propagate it through social networks? How can KR methods help in analyzing the social networks? What are the challenges in representing social networks and using inference to analyze them?