An Efficient RDF Converter and SPARQL Endpoint for the Complete OpenStreetMap Data

Bast, Hannah; Brosi, Patrick; Kalmbach, Johannes; Lehmann, Axel

doi:10.1145/3474717.3484256

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…For the second scenario, we focused on the outdoor area around the express bus terminal. Here, we utilized data from OSMTTL [50], recognized as the most accessible knowledge graph in South Korea for spatial RDF information. This data was seamlessly merged with the RDF dataset generated through our approach.…”

Section: Visualization and Case Studymentioning

confidence: 99%

“…The OSM node representing a motel can be connected to a node on the road nearby, and then by using the bloc:path predicate that represents the State-State connection inside the building, it is possible to For the second scenario, we focused on the outdoor area around the express bus terminal. Here, we utilized data from OSMTTL [50], recognized as the most accessible knowledge graph in South Korea for spatial RDF information. This data was seamlessly merged with the RDF dataset generated through our approach.…”

Section: Visualization and Case Studymentioning

confidence: 99%

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Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

Jang,

Yu,

Kim

2024

IJGI

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With the boom in online information, knowledge graphs like Freebase, Wikidata, and YAGO have emerged, thanks to the introduction of the RDF (Resource Description Framework). As RDF data grew, more and more spatial data was incorporated into it. While we have a lot of 2D data for outdoor spaces, mapping indoor spaces in 3D is challenging because it is expensive and time-consuming. In our research, we turned 2D blueprints into detailed 3D maps and then translated this into RDF format. We used the Jeonju Express Bus Terminal in South Korea as our test case. We made an automated tool that can turn 2D spatial data into 3D data that fits the IndoorGML standard. We also introduced terms like ‘loc’, ‘indoorgml-lite’, and ‘bloc’ to describe indoor spaces in the RDF format. Once we put our data into a GraphDB database, we could easily search for specific details and routes inside buildings. This work fills a significant gap in knowledge graphs concerning indoor spaces. However, the production of large-scale data across varied areas remains a challenge, pointing towards future research directions for more comprehensive indoor spatial information systems.

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Section: Visualization and Case Studymentioning

confidence: 99%

Section: Visualization and Case Studymentioning

confidence: 99%

Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

Jang,

Yu,

Kim

2024

IJGI

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“…We constructed the dataset using the OSM TTL RDF dataset [30] in the Singapore region. Unlike Questions1089, which chose Great Britain as the research area, we selected Singapore-a slightly smaller region but one rich in urban spatial features and with an English-speaking demographic.…”

Section: Datasetmentioning

confidence: 99%

Geographic Knowledge Base Question Answering over OpenStreetMap

Yang,

Jang,

2023

IJGI

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In recent years, question answering on knowledge bases (KBQA) has emerged as a promising approach for providing unified, user-friendly access to knowledge bases. Nevertheless, existing KBQA systems struggle to answer spatial-related questions, prompting the introduction of geographic knowledge ba se question answering (GeoKBQA) to address such challenges. Current GeoKBQA systems face three primary issues: (1) the limited scale of questions, restricting the effective application of neural networks; (2) reliance on rule-based approaches dependent on predefined templates, resulting in coverage and scalability challenges; and (3) the assumption of the availability of a golden entity, limiting the practicality of GeoKBQA systems. In this work, we aim to address these three critical issues to develop a practical GeoKBQA system. We construct a large-scale, high-quality GeoKBQA dataset and link mentions in the questions to entities in OpenStreetMap using an end-to-end entity-linking method. Additionally, we develop a query generator that translates natural language questions, along with the entities predicted by entity linking into corresponding GeoSPARQL queries. To the best of our knowledge, this work presents the first purely neural-based GeoKBQA system with potential for real-world application.

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“…Typically, approaches aim at establishing identity links between the different representations of geographic entities and concepts in these sources. For example, [10] proposes a pipeline for link discovery between OSM, Wikidata and DBpedia based on OSM tags, [4] aligns the schema between these sources using an adversarial classifier, and osm2rdf converts the whole OpenStreetMap data to RDF triples [1]. In contrast to these approaches, our task deals with persons, a class of entities not present in OSM.…”

Section: Connecting Osm With Knowledge Graphsmentioning

confidence: 99%

“…Streets are often named after famous or distinguished individuals who may or may not have a direct connection to the specific location. While geographic data sources such as OpenStreetMap (OSM) 1 contain information about streets across the globe and knowledge graphs such as Wikidata contain information about famous individuals and their relations, these two worlds are often not connected. This makes it challenging to connect streets to whom they are named after.…”

Section: Introductionmentioning

confidence: 99%

Linking Streets in OpenStreetMap to Persons in Wikidata

Gurtovoy

Gottschalk

2022

Companion Proceedings of the Web Conference 2022

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Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180, 000 links between streets and persons.

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An Efficient RDF Converter and SPARQL Endpoint for the Complete OpenStreetMap Data

Cited by 8 publications

References 4 publications

Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

Generating Spatial Knowledge Graphs with 2D Indoor Floorplan Data: A Case Study on the Jeonju Express Bus Terminal

Geographic Knowledge Base Question Answering over OpenStreetMap

Linking Streets in OpenStreetMap to Persons in Wikidata

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