50 research outputs found
Recommended from our members
ADCN: An Anisotropic Density-Based Clustering Algorithm for Discovering Spatial Point Patterns with Noise
Density-based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared to other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a search radius Eps to find at least MinPts points as a criterion for deriving local density is easily understandable and sufficient for exploring isotropic spatial point patterns. However, there are many cases that cannot be adequately captured this way, particularly if they involve linear features or shapes with a continuously changing density such as a spiral. In such cases, DBSCAN tends to either create an increasing number of small clusters or add noise points into large clusters. Therefore, in this paper, we propose a novel anisotropic density-based clustering algorithm (ADCN). To motivate our work, we introduce synthetic and real-world cases that cannot be sufficiently handled by DBSCAN (and OPTICS). We then present our clustering algorithm and test it with a wide range of cases. We demonstrate that our algorithm can perform as equally well as DBSCAN in cases that do not explicitly benefit from an anisotropic perspective and that it outperforms DBSCAN in cases that do. We show that our approach has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index and O(n 2 ) otherwise. We provide an implementation and test the runtime over multiple cases. Finally, we apply DBSCAN, OPTICS, and ADCN to the task of extracting urban areas of interest (AOI) from geotagged photos in six cities. Visual comparison shows that, comparing to DBSCAN and OPTICS, ADCN is inclined to extract AOIs with linear shapes which follow the underline road networks. ADCN also turns out to connect clusters when the spatial distribution of them shows similar directions
POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset
Many services that perform information retrieval for Points of Interest (POI)
utilize a Lucene-based setup with spatial filtering. While this type of system
is easy to implement it does not make use of semantics but relies on direct
word matches between a query and reviews leading to a loss in both precision
and recall. To study the challenging task of semantically enriching POIs from
unstructured data in order to support open-domain search and question answering
(QA), we introduce a new dataset POIReviewQA. It consists of 20k questions
(e.g."is this restaurant dog friendly?") for 1022 Yelp business types. For each
question we sampled 10 reviews, and annotated each sentence in the reviews
whether it answers the question and what the corresponding answer is. To test a
system's ability to understand the text we adopt an information retrieval
evaluation by ranking all the review sentences for a question based on the
likelihood that they answer this question. We build a Lucene-based baseline
model, which achieves 77.0% AUC and 48.8% MAP. A sentence embedding-based model
achieves 79.2% AUC and 41.8% MAP, indicating that the dataset presents a
challenging problem for future research by the GIR community. The result
technology can help exploit the thematic content of web documents and social
media for characterisation of locations
Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
Recently, several studies have explored methods for using KG embedding to
answer logical queries. These approaches either treat embedding learning and
query answering as two separated learning tasks, or fail to deal with the
variability of contributions from different query paths. We proposed to
leverage a graph attention mechanism to handle the unequal contribution of
different query paths. However, commonly used graph attention assumes that the
center node embedding is provided, which is unavailable in this task since the
center node is to be predicted. To solve this problem we propose a multi-head
attention-based end-to-end logical query answering model, called Contextual
Graph Attention model(CGA), which uses an initial neighborhood aggregation
layer to generate the center embedding, and the whole model is trained jointly
on the original KG structure as well as the sampled query-answer pairs. We also
introduce two new datasets, DB18 and WikiGeo19, which are rather large in size
compared to the existing datasets and contain many more relation types, and use
them to evaluate the performance of the proposed model. Our result shows that
the proposed CGA with fewer learnable parameters consistently outperforms the
baseline models on both datasets as well as Bio dataset.Comment: 8 pages, 3 figures, camera ready version of article accepted to K-CAP
2019, Marina del Rey, California, United State
EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System
Over the past decade, the electric vehicle industry has experienced
unprecedented growth and diversification, resulting in a complex ecosystem. To
effectively manage this multifaceted field, we present an EV-centric knowledge
graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial
knowledge management system. The EVKG encapsulates essential EV-related
knowledge, including EV adoption, electric vehicle supply equipment, and
electricity transmission network, to support decision-making related to EV
technology development, infrastructure planning, and policy-making by providing
timely and accurate information and analysis. To enrich and contextualize the
EVKG, we integrate the developed EV-relevant ontology modules from existing
well-known knowledge graphs and ontologies. This integration enables
interoperability with other knowledge graphs in the Linked Data Open Cloud,
enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six
competency questions, we demonstrate how the EVKG can be used to answer various
types of EV-related questions, providing critical insights into the EV
ecosystem. Our EVKG provides an efficient and effective approach for managing
the complex and diverse EV industry. By consolidating critical EV-related
knowledge into a single, easily accessible resource, the EVKG supports
decision-makers in making informed choices about EV technology development,
infrastructure planning, and policy-making. As a flexible and extensible
platform, the EVKG is capable of accommodating a wide range of data sources,
enabling it to evolve alongside the rapidly changing EV landscape
xNet+SC: Classifying Places Based on Images by Incorporating Spatial Contexts
With recent advancements in deep convolutional neural networks, researchers in geographic information science gained access to powerful models to address challenging problems such as extracting objects from satellite imagery. However, as the underlying techniques are essentially borrowed from other research fields, e.g., computer vision or machine translation, they are often not spatially explicit. In this paper, we demonstrate how utilizing the rich information embedded in spatial contexts (SC) can substantially improve the classification of place types from images of their facades and interiors. By experimenting with different types of spatial contexts, namely spatial relatedness, spatial co-location, and spatial sequence pattern, we improve the accuracy of state-of-the-art models such as ResNet - which are known to outperform humans on the ImageNet dataset - by over 40%. Our study raises awareness for leveraging spatial contexts and domain knowledge in general in advancing deep learning models, thereby also demonstrating that theory-driven and data-driven approaches are mutually beneficial
Narrative Cartography with Knowledge Graphs
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
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
Geo-tagged images are publicly available in large quantities, whereas labels
such as object classes are rather scarce and expensive to collect. Meanwhile,
contrastive learning has achieved tremendous success in various natural image
and language tasks with limited labeled data. However, existing methods fail to
fully leverage geospatial information, which can be paramount to distinguishing
objects that are visually similar. To directly leverage the abundant geospatial
information associated with images in pre-training, fine-tuning, and inference
stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised
learning framework for geo-tagged images. We use a dual-encoder to separately
encode the images and their corresponding geo-locations, and use contrastive
objectives to learn effective location representations from images, which can
be transferred to downstream supervised tasks such as image classification.
Experiments show that CSP can improve model performance on both iNat2018 and
fMoW datasets. Especially, on iNat2018, CSP significantly boosts the model
performance with 10-34% relative improvement with various labeled training data
sampling ratios.Comment: In: ICML 2023, Jul 23 - 29, 2023, Honolulu, Hawaii, US
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a
variety of downstream tasks such as summarization, link prediction, information
retrieval, and question answering. However, most existing KG embedding models
neglect space and, therefore, do not perform well when applied to (geo)spatial
data and tasks. For those models that consider space, most of them primarily
rely on some notions of distance. These models suffer from higher computational
complexity during training while still losing information beyond the relative
distance between entities. In this work, we propose a location-aware KG
embedding model called SE-KGE. It directly encodes spatial information such as
point coordinates or bounding boxes of geographic entities into the KG
embedding space. The resulting model is capable of handling different types of
spatial reasoning. We also construct a geographic knowledge graph as well as a
set of geographic query-answer pairs called DBGeo to evaluate the performance
of SE-KGE in comparison to multiple baselines. Evaluation results show that
SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic
query answering task. This demonstrates the effectiveness of our
spatially-explicit model and the importance of considering the scale of
different geographic entities. Finally, we introduce a novel downstream task
called spatial semantic lifting which links an arbitrary location in the study
area to entities in the KG via some relations. Evaluation on DBGeo shows that
our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI