Knowledge Tree
WorldKG
Also known as: World KG
Facts (11)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 11 facts
claimWorldKG and HKGB utilize semi-automatic methods to build initial ontologies, which is more advanced than manual ontology construction.
claimKnowledge graph solutions often use rule-based mappings to extract entities and relations from semi-structured sources, as seen in DBpedia, Yago, DRKG, VisualSem, and WorldKG.
procedureThe second part of the WorldKG construction process consists of three steps: 1) filter nodes with at least one tag, 2) filter keys and values based on the initial ontology, and 3) create RDF triples.
procedureThe construction of WorldKG involves two parts: first, creating an initial ontology based on OpenStreetMap's 'Map' system and wiki data; second, mapping OpenStreetMap data to the final knowledge graph structure.
referenceWorldKG is a world-scale geographic knowledge graph presented by A. Dsouza et al. at the 30th ACM International Conference on Information & Knowledge Management in 2021.
claimThe World KG approach to knowledge graph construction manually verifies all matches to external ontologies for quality assurance.
measurementThe WorldKG knowledge graph, established in 2021, contains 113 million entities and 829 million facts, utilizing RDF format.
claimWorldKG utilizes an unsupervised machine learning approach for ontology alignment, whereas most other knowledge graph approaches perform alignment and merging of ontologies manually.
claimDRKG and WorldKG represent one-time efforts in knowledge graph construction without any updates.
claimWorldKG integrates semi-structured data from OpenStreetMap into a geographic knowledge graph.
procedureThe WorldKG ontology construction process involves fetching geographic class information tags from OpenStreetMap, using key-value pairs to infer a class hierarchy, and aligning these classes with Wikidata and DBpedia using an unsupervised machine learning approach followed by manual verification.