Call for Papers: Knowledge Modeling in Weather and Climate Sciences
Sharing this AMS opportunity for my colleague Jon Mote in the Weather Program Office at NOAA OAR. Please reach out to jonathan.mote@noaa.gov for more information. the American Meteorological Society’s 20th Symposium on Societal Applications: Policy, Research and Practice January 12 - 16, 2025 in New Orleans, LA. Session Topic ID: 69236 Session Topic Title: Navigating Complexity: Knowledge Modeling and Graphs in the Weather, Climate, and Social Sciences Conference: 20th Symposium on Societal Applications: Policy, Research and Practice To submit an abstract, simply select your conference name (as listed above) here. The abstract submission portal will open by 13 June 2024. The deadline for all submissions is 15 August 2024 at 05:00 PM ET. Session Description: This session will gather four to five papers that explore the theoretical foundations, practical implementations, and future directions of knowledge modeling and graphs in the context of weather, climate, and social sciences. The goal of the session is to foster a deeper understanding of their pivotal role in navigating the complexities inherent in atmospheric and environmental systems. By examining diverse perspectives and methodologies, participants will cultivate insights into how knowledge modeling and graphs can enhance interdisciplinary collaboration and inform evidence-based decision-making in addressing climate-related challenges. In today’s world, the management of ever-increasing amounts of data is a significant challenge. To tackle this, we need new ways of organizing and analyzing information. Knowledge modeling and knowledge graph approaches have emerged as important tools to address this challenge. Knowledge modeling involves organizing information into structured formats to represent knowledge, while knowledge graphs utilize graph structures to model and organize interconnected knowledge entities and relationships. Together, these approaches offer a more efficient way to understand and work with large amounts of data. Using knowledge modeling and graphs, researchers can make sense of complex data more quickly and easily. In the weather, climate, and social sciences, the utilization of knowledge modeling and graph-based approaches is still limited. But the increasing volume, velocity, and variety of weather, climate, and societal data poses significant challenges for data storage, management, and analysis, both now and in the future. In this regard, knowledge modeling and knowledge graphs emerge as not only beneficial options, but imperative directions to take. This session serves as a platform to delve into the multifaceted applications and implications of knowledge modeling and graphs within the weather, climate, and societal data domains. By harnessing these methodologies, researchers gain the ability to meticulously organize, succinctly represent, and effectively traverse the vast and intricate web of data, concepts, and relationships inherent in weather, climate, and societal phenomena. Through a synthesis of theoretical discussions exploring the underpinnings of these methodologies and practical case studies showcasing their real-world utility, this session endeavors to highlight the transformative potential of knowledge modeling and graphs in advancing our understanding, predicting, and responding to the dynamic forces shaping our atmospheric and environmental systems. Potential Questions for Presenters:
How can knowledge modeling techniques, such as ontologies and semantic networks, enhance the representation and organization of weather, climate and societal data and concepts?
In what ways can knowledge graphs facilitate interdisciplinary collaboration and knowledge exchange among stakeholders in weather, climate, and societal research?
How can knowledge graphs support predictive modeling and forecasting efforts in weather, climate, and societal sciences, and what are the associated benefits and limitations?
What strategies can be employed to integrate diverse data sources and types into knowledge graphs, ensuring comprehensive coverage and accuracy?
How can knowledge graphs contribute to improving the accessibility and interpretability of weather, climate, and societal data for policymakers, stakeholders, and the general public?
What are some of the challenges or blockers (e.g. organizational or technological) that have hindered the implementation of knowledge modeling efforts?
Topic Keywords: Collaboration, Data Analytics, Data Applications, Social, Behavioral, and Economic Sciences, data discovery
