Narrative Cartography with Knowledge Graphs
- URL: http://arxiv.org/abs/2112.00970v1
- Date: Thu, 2 Dec 2021 04:01:17 GMT
- Title: Narrative Cartography with Knowledge Graphs
- Authors: Gengchen Mai, Weiming Huang, Ling Cai, Rui Zhu, Ni Lao
- Abstract summary: We propose the idea of narrative cartography with knowledge graphs (KGs)
To tackle the data acquisition & integration challenge, we develop a set of KG-based GeoEnrichment toolboxes.
With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format.
- Score: 10.715484138543069
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Narrative cartography is a discipline which studies the interwoven nature of
stories and maps. However, conventional geovisualization techniques of
narratives often encounter several prominent challenges, including the data
acquisition & integration challenge and the semantic challenge. To tackle these
challenges, in this paper, we propose the idea of narrative cartography with
knowledge graphs (KGs). Firstly, to tackle the data acquisition & integration
challenge, we develop a set of KG-based GeoEnrichment toolboxes to allow users
to search and retrieve relevant data from integrated cross-domain knowledge
graphs for narrative mapping from within a GISystem. With the help of this
tool, the retrieved data from KGs are directly materialized in a GIS format
which is ready for spatial analysis and mapping. Two use cases - Magellan's
expedition and World War II - are presented to show the effectiveness of this
approach. In the meantime, several limitations are identified from this
approach, such as data incompleteness, semantic incompatibility, and the
semantic challenge in geovisualization. For the later two limitations, we
propose a modular ontology for narrative cartography, which formalizes both the
map content (Map Content Module) and the geovisualization process (Cartography
Module). We demonstrate that, by representing both the map content and the
geovisualization process in KGs (an ontology), we can realize both data
reusability and map reproducibility for narrative cartography.
Related papers
- Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
We introduce a comprehensive geolocation framework with three key components.
GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.
We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
arXiv Detail & Related papers (2025-02-19T14:21:25Z) - PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [64.58959634712215]
Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface.
Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding.
To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
arXiv Detail & Related papers (2025-01-10T18:59:42Z) - Map2Text: New Content Generation from Low-Dimensional Visualizations [60.02149343347818]
We introduce Map2Text, a novel task that translates spatial coordinates within low-dimensional visualizations into new, coherent, and accurately aligned textual content.
This allows users to explore and navigate undiscovered information embedded in these spatial layouts interactively and intuitively.
arXiv Detail & Related papers (2024-12-24T20:16:13Z) - The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs [4.358099505067763]
This paper outlines the implementation of Google's S2 Geometry within KnowWhereGraph.
Ultimately, this work demonstrates the potential of DGGS frameworks, particularly S2, for building scalable GeoKGs.
arXiv Detail & Related papers (2024-10-18T18:30:05Z) - Application of Disentanglement to Map Registration Problem [0.3277163122167434]
It is essential to first align different "styles" of geospatial data to its matching images that point to the same location on the surface of the Earth.
We propose a combination of $beta$-VAE-like architecture and adversarial training to achieve both the disentanglement of the geographic information and artistic styles and generation of new map tiles.
arXiv Detail & Related papers (2024-08-26T09:55:32Z) - Detecting Omissions in Geographic Maps through Computer Vision [18.36056648425432]
We develop and evaluate a method for automatically identifying maps that depict specific regions and feature landmarks with designated names.
We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names.
Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks.
arXiv Detail & Related papers (2024-07-15T13:26:58Z) - Towards Vision-Language Geo-Foundation Model: A Survey [65.70547895998541]
Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks.
This paper thoroughly reviews VLGFMs, summarizing and analyzing recent developments in the field.
arXiv Detail & Related papers (2024-06-13T17:57:30Z) - Geospatial Knowledge Graphs [3.0638648756719222]
Geospatial knowledge graphs have emerged as a novel paradigm for representing and reasoning over geospatial information.
This entry first introduces key concepts in knowledge graphs along with their associated standardization and tools.
It then delves into the application of knowledge graphs in geography and environmental sciences.
arXiv Detail & Related papers (2024-05-13T11:45:22Z) - GeoDecoder: Empowering Multimodal Map Understanding [3.164495478670176]
GeoDecoder is a dedicated multimodal model designed for processing geospatial information in maps.
Built on the BeitGPT architecture, GeoDecoder incorporates specialized expert modules for image and text processing.
arXiv Detail & Related papers (2024-01-26T02:39:40Z) - GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE.
We collect data from open-released geographic resources and introduce six natural language understanding tasks.
We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
arXiv Detail & Related papers (2023-05-11T03:21:56Z) - Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection.
The proposed method constructs graph signals leveraging both local image features and global shape features.
Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
arXiv Detail & Related papers (2020-04-17T11:55:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.