Second International Workshop on Capturing Scientific Knowledge
Austin, Texas, USA
The aim of SciKnow 2017 is to bring together researchers interested in representing and capturing knowledge about science so that it can be used by intelligent systems to support scientific research and discovery.
From the early days of Artificial Intelligence, researchers have been interested in capturing scientific knowledge to develop intelligent systems. There are a variety of formalisms used today in different areas of science. Ontologies are widely used for organizing knowledge, particularly in biology and medicine. Process representations are used to do qualitative reasoning in areas such as physics and chemistry. Probabilistic graphical models are used by machine learning researchers, e.g., in climate modeling.
In addition to enabling more advanced capabilities for intelligent systems in science, capturing scientific knowledge enables knowledge dissemination and open science practices. This is increasingly more important to enable the reuse of scientific knowledge across scientific disciplines, businesses and the public.
Although great advances have been made, scientific knowledge is complex and poses great challenges for knowledge capture. This workshop will provide a forum to discuss existing forms of scientific knowledge representation and existing systems that use them, and to envision major areas to augment and expand this important field of research.
The increasing emphasis in open science has had a major focus on data sharing but it needs to encompass knowledge as well. There are many research challenges in open sharing and reuse of scientific knowledge that need to be addressed in future research.
SciKnow2017 is the second version of a series, which started at K-CAP 2015 with a full day event (https://www.isi.edu/ikcap/sciknow2015/).
Major topics of interest for this workshop include:
- Capture of scientific knowledge:
- Successful knowledge capture and representation formalisms are used in a variety of scientific domains, what are their key features and merits?
- Scientific knowledge is inherently complex and requires significant effort to capture. What are effective approaches to model and to acquire scientific knowledge?
- Representation of scientific knowledge:
- Given the variety of representation formalisms for scientific knowledge, how can they be combined to enable more advanced capabilities?
- What approaches can support the uncertainty and evolution inherent in scientific models?
- (Re)use of scientific knowledge:
- Imagine what scientific breakthroughs might be enabled with improved representational schema of existing scientific knowledge, and of course the subsequent capture of additional scientific knowledge.
- What are effective approaches that enable open sharing, dissemination, and reuse of scientific knowledge?
Submissions can be made in the following categories:
- Report papers: Overviews or summaries of past work on approaches to represent and capture scientific knowledge.
- Research papers: Novel results of research on scientific knowledge representation or capture.
- Position papers: Discussion on issues concerning the representation, capture, and dissemination of scientific knowledge, particularly to facilitate cross-disciplinary integrative science.
- Challenge papers: Specific scenarios that describe the benefits to science if the limitations identified are overcome.
Accepted papers will be made available on the workshop site.
- Submission deadline: September 17, 2017
- Notifications to authors: October 1st, 2017
- Workshop: December 4, 2017
- Daniel Garijo, University of Southern California
- Martine de Vos, Netherlands eScience Center
- Yolanda Gil, University of Southern California
- Tim Clark, Harvard University
- Derek Sleeman, University of Aberdeen
- Idafen Santana, Universidad Politecnica de Madrid
- Olga Ximena Giraldo, Universidad Politecnica de Madrid
- Alexander Garcia Castro, Universidad Politecnica de Madrid
- Gully Burns, University of Southern California
- Tobias Kuhn, VU University Amsterdam
- Paul Groth, Elsevier Labs
- Silvio Peroni, University of Bologna
- Willem van Hage, Netherlands eScience center
- Jan Top, VU University Amsterdam
- Richard Boyce, University of Pittsburgh