This workshop will be held on Sept. 27, 2021 (CEST).
|Opening Introduction||15:30 - 16:00||Gengchen Mai, Ling Cai||Geospatial Knowledge Graph and Spatially-Explicit AI|
|Keynote||16:00 - 17:00||Pascal Hitzler||The KnowWhereGraph|
|Break||17:00 - 17:10|
|Session 1: Geospatial Semantics and GeoKG||17:10 - 17:30||Shirly Stephen, Wenwen Li and Torsten Hahmann||Geo-Situation for Modeling Causality of Geo-Events in Knowledge Graphs|
|17:30 - 17:50||Marvin Mc Cutchan and Ioannis Giannopoulos||Geospatial Semantics and Geographic Aware ANN|
|17:50 - 18:10||Yuanyuan Tian and Wenwen Li||GeoAI for Knowledge Graph Construction: Identifying Causality Between Cascading Events to Support Environmental Resilience Research|
|Break||18:10 - 18:20|
|Session 2: GeoAI||18:20 - 18:40||Peng Yue, Boyi Shangguan, Lei Hu, Chenxiao Zhang, Liangcun Jiang and Zhe Fang||Quality Considerations for AI Training Data in Remote Sensing|
|18:40 - 19:00||Jin Xing and Renee Sieber||Challenges of Using XAI for Geographic Data Analytics|
|19:00 - 19:20||Cláudia Rodrigues, Ana Alves, Marco Veloso and Carlos Bento||Identification of the User’s Geographic Map|
|19:20 - 19:40||Haojian Liang and Shaohua Wang||A New Approach Based on Graph Neural Network for Solving p-center Problems|
|Discussion & Closing Remark||19:40 - 20:00||PC Chair|
The rapid increase in high-quality data, advanced machine learning algorithms, and the availability of fast hardware have largely contributed to a renewed interest in Artificial Intelligence (AI). Despite many successful stories in computer vision, natural language processing, and speech recognition, there are many challenges that remain to be solved, such as large scale neural symbolic reasoning based on unstructured text and automatic knowledge graph construction. Interestingly, nowadays, one of the most prominent topics in AI is the combination of representation learning techniques (Connectionist Artificial Intelligence) with symbolic representation and reasoning associated with knowledge graphs (Symbolic Artificial Intelligence), in order to develop scalable and interpretable machine learning models. One good example is knowledge graph embedding models that aim at representing components of knowledge graphs, such as entities and relations, as continuous vectors or matrices while preserving the graph’s structural information. From a geospatial point-of-view, GeoAI, as an interdisciplinary field of GIScience and AI, advocates the idea of developing and utilizing AI techniques in geography and earth science. Geospatial knowledge graphs, as symbolic representations of geospatial knowledge, go to the core of GeoAI and facilitate many intelligent applications such as geospatial data integration and knowledge discovery. In fact, geospatial data plays an important role in the Linked Open Data cloud, an open-sourced cross-domain knowledge graph, since spatio-temporal scopes are essential for describing events, people, and objects. However, many relational machine learning models treat geographic entities as ordinary entities in which the spatial footprints of places are neglected and the distance decay effect is ignored. This results in suboptimal performance in many geospatial related tasks such as geospatial knowledge graph completion, geographic question answering, geographic entity alignment, as well as geographic knowledge graph summarization.
In addition, there exist many demands for further advancements in other research topics related to GeoAI, such as remote sensing and street view image analysis, transportation modeling, and geo-text analysis. There are, for instance, many challenges in the adaptation of deep learning techniques to these scenarios, including the limited availability of labeled data or the difficulty of the models to generalize between locations. Incorporating geo-spatial knowledge (i.e., prior knowledge about the structure of objects on the surface of the Earth, and about the fundamental rules of geography) directly into deep neural network models, in the form of specially designed components and/or regularization schemes, is a promising approach to address the aforementioned challenges.
Based on the above observations, this combined workshop and tutorial emphasizes the importance of geospatial information and principles in designing, developing, and utilizing geospatial knowledge graphs and other GeoAI techniques. Accordingly, we call for new methods, models, and resources for advancing research related to Geospatial Knowledge Graphs and GeoAI.
This workshop will have a half-day for tutorial sessions and a half-day for research presentations. We welcome short research articles and industry demonstration papers regarding relevant topics. The page limit is 4 pages and the recommended template is the 2019 template provided by LIPIcs (http://drops.dagstuhl.de/styles/lipics-v2019/lipics-v2019-authors.tgz). The submission Web page for both tracks of GIScience 2021 is: https://easychair.org/conferences/?conf=geokg21.
A special issue, with the same scope as this workshop, will be published in Transactions in GIS (TGIS). Submissions to the special issue may be made by either the workshop participants or others interested in the theme. Participation in the workshop does not guarantee acceptance in the special feature, and all submissions will be submitted to a review process that follows TGIS standards. However, we strongly recommend researchers first submit their initial work to this workshop as a preselection step.
The full papers will be due on Feb 15, 2022, thus allowing to incorporate feedback and new insights gained at the workshop, and potentially even forming new teams of authors. Please refer to the official CFP of this special issue from TGIS webpage and this PDF.