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Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs
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2020 | Decision Support Systems | Citations: 0
Authors: Nizzoli, Leonardo; Avvenuti, Marco; Tesconi, Maurizio; Cresci, Stefano

Abstract: Online social networks convey rich information about geospatial facets of realit ... Expand

Abstract: Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10 k event-related tweets, achieving F1 = 0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques. Collapse

Semantic filters: Equatorial Guinea

Topics: knowledge graph geographic information system Twitter knowledge representation decision support system

Methods: experiment computational algorithm regression analysis method DBpedia BERT
An alternative measure of the ICT-Opportunity Index
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2010 | Information & Management | Citations: 0
Authors: Emrouznejad, Ali; Cabanda, Emilyn; Gholami, Roya

Abstract: We developed an alternative approach for measuring information and communication ... Expand

Abstract: We developed an alternative approach for measuring information and communication technology (ICT), applying Data Envelopment Analysis (DEA) using data from the International Telecommunications Union as a sample of 183 economies. We compared the ICT-Opportunity Index (ICT-OI) with our DEA-Opportunity Index (DEA-OI) and found a high correlation between the two. Our findings suggest that both indices are consistent in their measurement of digital opportunity, though differences still exist in different regions. Our new DEA-OI offers much more than the ICT-OI. Using our model, the target and peer groups for each country can be identified. Collapse

Semantic filters: Equatorial Guinea

Topics: decision making IT policy information technology infrastructure communication service infrastructure

Methods: descriptive statistic trade-off analysis method data envelopment analysis

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Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs

An alternative measure of the ICT-Opportunity Index