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[62] Knowledge Graph for Dunhuang Cultural Heritage

Knowledge Graph / Semantic Infrastructure Topology & Graph Data Infrastructure 2020

Paper Description extracted from PDF · abstract · 147 words

This study employs a knowledge graph approach to re alize the representation and association of information resources, promote the research, teaching, and dissemination of Dunhuang cultural heritage (CH). The Dunhuang Mogao Grottoes is a UNESCO world CH site, and digitization of Dunhuang CH has produced a large amount of information resources. Howeve r, these digitized resources continue to lack the systematic granular semantic representation required to correlate Dunhuang cultural heritage information (CHI) in order to facilitate efficient research and appreciation. To respond to this need, new approaches for representing CHI are being developed. This study identifies five facets and their semantic relationship to Dunhuang CH, constructs an ontology model to regulate the entities, attri butes, and relationships of Dunhuang CH knowledge, and subsequently processes the resulting data using various techniques (such as semantic annotation and entity association) to facilitate rendering the data in a knowledge graph construction.

AuthorsWang, X.; Gu, K.; Song, K.; Zhang, X.
Year2020
VenueHeritage Science / Conference Publication
DOI10.5771/0943-7444-2020-7-604 (auto-fetched · OpenAlex)
Source DatabaseBacktracking
Bridge-to-GNNCategory C
GNN ArchitectureKnowledge Graph Embedding (inferred from title)
Graph EncodingKnowledge Graph / Ontological Network (inferred from title)
AEC TaskKnowledge Graph Construction (inferred from title)
CohortEarly Infrastructure (2020–2021)

Extracted Methodology heuristic — verify before citing

Implementation FrameworkNeo4j (heuristic — verify)
Primary MetricAccuracy (heuristic — verify)

Detected Technical Stack auto-detected from PDF · regex catalogue match

GNN architectures detectedKnowledge Graph Embedding (TransE/RotatE/DistMult)
Frameworks / librariesNeo4j / Cypher
Benchmark datasets referencedCityGML
Quality (heuristic, 6-flag)1/6 · rigor: Low
Code/Data: —Benchmark: ✓Baseline: —CV/Split: —Ablation: —Reproducible: —

Connected Papers Verification bibliometric (data/connected_papers_matches.json)

Bibliometrically related to other seedsThis paper appeared in the Connected Papers neighbourhood of these corpus seeds:

[6] 2021 · Akroyd · Universal Digital Twin – A Dynamic Knowledge Graph

Lineage Families

topology-infrastructure (auto-suggested)

Enabled By / Precursors

None tracked

Extended By

None in corpus

Infrastructure Dependencies

Knowledge Graph (RDF/OWL/SPARQL) (auto-suggested) Neo4j / Cypher (detected from PDF)

Research Frontier Tags

knowledge-graph

Part of the GML/GNN in AEC Systematic Review (PRISMA 2020) — 112 papers, 2020–2026