Artificial Intelligence and Deep Learning Symposium: Geospatial Semantics and Geo-Text Data Analytics II

Type: Paper
Theme:
Sponsor Groups:
Poster #:
Day: 4/12/2018
Start / End Time: 3:20 PM / 5:00 PM
Room: Grand Ballroom A, Astor, 2nd Floor
Organizers: Kevin Sparks, Yingjie Hu
Chairs: Kevin Sparks

Description

Geospatial semantics is a broad field that involves research ranging from geo-ontology engineering and gazetteer construction to geographic information retrieval (GIR) and place semantics. In recent years, there is an increasing number of datasets that contain both geographic locations and natural language texts, such as geotagged tweets, geotagged Wikipedia pages, geotagged housing advertisements, as well as daily descriptions and news articles that contain place names. These geo-text data offer new opportunities for research, since there is a variety of information that can be extracted from texts, such as place names, various entities, topics, opinions, and emotions, and we can examine the spatiotemporal patterns of the extracted information. Accordingly, mining geo-text data often needs an effective integration of geospatial analysis and natural language processing (NLP). With the recent advancements of AI and deep learning, new methods, such as Recurrent Neural Network (RNN), have also been employed to analyze texts and have received promising results. A combination of geo-text data with deep learning and other AI techniques may give birth to even more ideas. Applying these methods in fields such as disaster response, land use mapping, public health, and population distribution (etc.) can provide novel and impactful solutions to existing and new geographic problems. Fundamentally, geo-text data analytics is situated under the umbrella of geospatial semantics, since effective data analysis needs an understanding of the semantics of texts (e.g., the topics, mentioned entities, and emotions) and its association with geospatial locations. This special session aims to bring together researchers with similar interests to exchange ideas and discuss future directions.

Example topics (include but not limited to):
- Spatial, temporal, and semantic analytics on geotagged social media data
- Deep learning or other techniques for extracting and disambiguating place names from texts
- Deep learning or other techniques for extracting spatial relations from texts
- Deep learning or other techniques for geo-text data classification
- Sentiment analysis and geospatial clustering based on geo-text data
- Topic modeling and geospatial analysis with geo-text data
- Gazetteer construction with place entries extracted from geo-text data
- New approaches for integrating geospatial analysis with natural language processing
- …


To present your research in our session, please submit your abstract through AAG website (http://www.aag.org/cs/annualmeeting/register) and send your PIN to Yingjie Hu (yhu21@utk.edu) or Kevin Sparks (sparkska@ornl.gov) by October 25, 2017.


Agenda

Type Details Minutes Start Time
Presenter Gengchen Mai*, University of California - Santa Barbara, CA, A Geographic Information Retrieval Framework Based on Semantic Web Technologies and Representation Learning 20 3:20 PM
Presenter Yingjie Hu*, University of Tennessee Knoxville, An empirical study on the names of points of interest and their changes with geographic distance 20 3:40 PM
Presenter Yongyao Jiang*, George Mason university, Yun Li, George Mason University, Chaowei Yang, George Mason University, A Smart Web-based Geospatial Data Discovery System for Oceanography 20 4:00 PM
Presenter Hu Shao*, Arizona State University, Wenwen Li, Arizona State University, A Geo-Cyberinfrastructure for One-Stop Geospatial Data Integration and Semantic Search 20 4:20 PM
Presenter Wei Zhai*, University of Florida, Xueyin Bai, Nanjing University, Yu Shi, Northwest University, Urban Functional Area Identification based on Multi-Source Data: A Word2vec Approach 20 4:40 PM

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