Workshop Date Update: Due to the uncertain impacts of COVID-19 in next months, the organizing committee has decided to postpone the workshop (GeoKG & GeoAI 2020) in conjunction with the GIScience conference until Fall 2021.
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 2020 is: https://easychair.org/conferences/?conf=geokg2020.
All presented papers will be made available through CEUR-WS proceedings contingent upon authors' agreement. We will also consider a special issue with a journal.
1. Gengchen Mai, Krzysztof Janowicz, Ling Cai, Rui Zhu, Blake Regalia, Bo Yan, Meilin Shi, Ni Lao. (2020) SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting. Transactions in GIS. DOI:10.1111/TGIS.12629 [arxiv paper]
2. Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao. Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells, In: Proceedings of ICLR 2020, Apr. 26 - 30, 2020, Addis Ababa, ETHIOPIA . [OpenReview paper] [arxiv paper] [code] [video] [slides]
3. Gengchen Mai, Krzysztof Janowicz, Bo Yan, Simon Scheider. Deeply Integrating Linked Data with Geographic Information Systems. Transactions in GIS, 23(2019), 579-600. DOI:10.1111/tgis.12538 [code]
4. Gengchen Mai, Bo Yan, Krzysztof Janowicz, Rui Zhu. Relaxing Unanswerable Geographic Questions Using A Spatially Explicit Knowledge Graph Embedding Model, In: Proceedings of AGILE 2019, June 17 - 20, 2019, Limassol, Cyprus.
5. Yingjie Hu, Chengbin Deng, Zhou Zhou. (2019) A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environment. Annals of the American Association of Geographers, 109(4), 1052-1073.
6. Yingjie Hu, Song Gao, Dalton Lunga, Wenwen Li, Shawn Newsam & Budhendra Bhaduri. (2019) GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions. SIGSPATIAL Special, 11(2), 5-15.
7. Jimin Wang, Yingjie Hu, Kenneth Joseph. (2020) NeuroTPR: A Neuro-net ToPonym Recognition model for extracting locations from social media messages. Transactions in GIS, in press
8. Jimin Wang, Yingjie Hu (2019): Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers. Transactions in GIS, 23(6), 1393-1419.
9. Yuhao Kang, Song Gao, Robert E. Roth. (2019) Transferring Multiscale Map Styles Using Generative Adversarial Networks. International Journal of Cartography. 5, 115-141.
10. Ling Cai, Krzysztof Janowicz, Gengchen Mai, Bo Yan, Rui Zhu. (2020) Traffic Transformer: Capturing the Continuity and Periodicity of Time Series for Traffic Forecasting. Transactions in GIS. in press
11. Bo Yan, Krzysztof Janowicz, Gengchen Mai, Rui Zhu. A Spatially-Explicit Reinforcement Learning Model for Geographic Knowledge Graph Summarization. Transactions in GIS, 23(2019), 620-640. DOI:10.1111/tgis.12547
12. Bo Yan, Krzysztof Janowicz, Gengchen Mai, Song Gao. From ITDL to Place2Vec -- Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts, In: Proceedings of the 25th International Conference on Advances in Ge-ographic Information Systems (ACM SIGSPATIAL 2017), November 7 - 10, 2017, Redondo Beach, California, USA.
13. Krzysztof Janowicz, Song Gao, Grant McKenzie, Yingjie Hu, and Budhendra Bhaduri. GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond., International Journal of Geographical Information Science 34(2020): 625-636.