Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLinking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information
94
0
0.0
(
0
)
Added by
Nicolas Tempelmeier
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information that has a high potential to complement these representations. However, identity links between the knowledge graph entities and OSM nodes are still rare. The problem of link discovery in these settings is particularly challenging due to the lack of a strict schema and heterogeneity of the user-defined node representations in OSM. In this article, we propose OSM2KG - a novel link discovery approach to predict identity links between OSM nodes and geographic entities in a knowledge graph. The core of the OSM2KG approach is a novel latent, compact representation of OSM nodes that captures semantic node similarity in an embedding. OSM2KG adopts this latent representation to train a supervised model for link prediction and utilises existing links between OSM and knowledge graphs for training. Our experiments conducted on several OSM datasets, as well as the Wikidata and DBpedia knowledge graphs, demonstrate that OSM2KG can reliably discover identity links. OSM2KG achieves an F1 score of 92.05% on Wikidata and of 94.17% on DBpedia on average, which corresponds to a 21.82 percentage points increase in F1 score on Wikidata compared to the best performing baselines.
rate research
Read More
OpenStreetMap (OSM) is one of the richest openly available sources of volunteered geographic information. Although OSM includes various geographical entities, their descriptions are highly heterogeneous, incomplete, and do not follow any well-defined ontology. Knowledge graphs can potentially provide valuable semantic information to enrich OSM entities. However, interlinking OSM entities with knowledge graphs is inherently difficult due to the large, heterogeneous, ambiguous, and flat OSM schema and the annotation sparsity. This paper tackles the alignment of OSM tags with the corresponding knowledge graph classes holistically by jointly considering the schema and instance layers. We propose a novel neural architecture that capitalizes upon a shared latent space for tag-to-class alignment created using linked entities in OSM and knowledge graphs. Our experiments performed to align OSM datasets for several countries with two of the most prominent openly available knowledge graphs, namely, Wikidata and DBpedia, demonstrate that the proposed approach outperforms the state-of-the-art schema alignment baselines by up to 53 percentage points in terms of F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is more than a 400% increase compared to the existing semantic annotations in OSM.
Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the extent to which traffic disruption can be estimated from static features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with static features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSMs granular point of interest data allows for better predictions than the aggregate categories typically used in studies of transportation and land use.
In an era of heterogeneous data, novel methods and volunteered geographic information provide opportunities to understand how people interact with a place. However, it is not enough to simply have such heterogeneous data, instead an understanding of its usability and reliability needs to be undertaken. Here, we draw upon the case study of Rakiura, Stewart Island where manifested passenger numbers across the Foveaux Strait are known. We have built a population model to ground truth such novel indicators. In our preliminary study, we find that a number of indicators offer the opportunity to understand fluctuations in populations. Some indicators (such as wastewater volumes) can suggest relative changes in populations in a raw form. While other indicators (such as TripAdvisor reviews or Instagram posts) require further data enrichment to get insights into population fluctuations. This research forms part of a larger research project looking to test and apply such novel indicators to inform disaster risk assessments.
Accurate modelling of local population movement patterns is a core contemporary concern for urban policymakers, affecting both the short term deployment of public transport resources and the longer term planning of transport infrastructure. Yet, while macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform badly in smaller geographic confines. In this paper we take a first step to remedying this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down at small scales. In particular, we use an anonymised aggregate dataset from a major mobility app and combine this with freely available data from OpenStreetMap concerning land-use composition of different areas around the county of Oxfordshire in the United Kingdom. We show where different models fail, and make the case for a new modelling strategy which moves beyond rough heuristics such as distance and population size towards a detailed, granular understanding of the opportunities presented in different areas of the city.
This paper investigates the problem of utilizing network topology and partial timestamps to detect the information source in a network. The problem incurs prohibitive cost under canonical maximum likelihood estimation (MLE) of the source due to the exponential number of possible infection paths. Our main idea of source detection, however, is to approximate the MLE by an alternative infection path based estimator, the essence of which is to identify the most likely infection path that is consistent with observed timestamps. The source node associated with that infection path is viewed as the estimated source $hat{v}$. We first study the case of tree topology, where by transforming the infection path based estimator into a linear integer programming, we find a reduced search region that remarkably improves the time efficiency. Within this reduced search region, the estimator $hat{v}$ is provably always on a path which we term as emph{candidate path}. This notion enables us to analyze the distribution of $d(v^{ast},hat{v})$, the error distance between $hat{v}$ and the true source $v^{ast}$, on arbitrary tree, which allows us to obtain for the first time, in the literature provable performance guarantee of the estimator under limited timestamps. Specifically, on the infinite $g$-regular tree with uniform sampled timestamps, we get a refined performance guarantee in the sense of a constant bounded $d(v^{ast},hat{v})$. By virtue of time labeled BFS tree, the estimator still performs fairly well when extended to more general graphs. Experiments on both synthetic and real datasets further demonstrate the superior performance of our proposed algorithms.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
