357 lines
19 KiB
YAML
357 lines
19 KiB
YAML
---
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# Citation Relationships Between Semantic Web Publications
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# Demonstrates citation linking patterns using CiTO (Citation Typing Ontology)
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# Links publications through different types of citation relationships
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- citation_id: https://w3id.org/heritage/citation/kg-2021-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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Uses Wikidata as a prominent example of a large-scale collaborative
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knowledge graph to illustrate key concepts in knowledge graph construction
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and evolution.
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citation_context: >-
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"Wikidata (Vrandečić and Krötzsch, 2014) is a free, collaborative knowledge
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base that serves as central storage for structured data of Wikimedia projects.
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It demonstrates how community-driven approaches can build comprehensive
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knowledge graphs at scale."
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page_number: "23"
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- citation_id: https://w3id.org/heritage/citation/lokg-2024-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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References the comprehensive knowledge graph survey to establish theoretical
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foundations for cultural heritage knowledge graph construction.
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citation_context: >-
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"Following the taxonomy proposed by Hogan et al. (2021), we structure the
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LOKG using established knowledge graph design patterns, adapting them for
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the specific requirements of cultural heritage domain modeling."
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page_number: "3"
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- citation_id: https://w3id.org/heritage/citation/lokg-2024-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: CITES_AS_EVIDENCE
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citation_intent: >-
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Uses Wikidata as evidence of successful collaborative knowledge base
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construction, informing the LOKG's crowdsourcing strategy.
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citation_context: >-
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"The success of Wikidata in enabling collaborative knowledge base construction
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(Vrandečić and Krötzsch, 2014) demonstrates the viability of community-driven
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approaches for cultural heritage metadata aggregation."
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page_number: "15"
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- citation_id: https://w3id.org/heritage/citation/iswc2024-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/iswc-2024-best-paper
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: DISCUSSES
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citation_intent: >-
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Discusses knowledge graph relationship modeling challenges identified in
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the survey, extending the analysis to dataset-level relationships.
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citation_context: >-
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"Hogan et al. (2021) identify relationship complexity as a fundamental
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challenge in knowledge graph design. Our work extends this analysis to
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the meta-level, examining relationships between datasets themselves rather
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than entities within datasets."
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page_number: "2"
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- citation_id: https://w3id.org/heritage/citation/iswc2024-cites-iswc2023
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citing_work: https://w3id.org/heritage/publication/iswc-2024-best-paper
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cited_work: https://w3id.org/heritage/publication/iswc-2023-best-paper
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citation_type: EXTENDS
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citation_intent: >-
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Extends spatial link prediction techniques to dataset relationship prediction,
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adapting embedding-based methods for a new domain.
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citation_context: >-
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"Building on recent advances in spatial link prediction with semantic
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embeddings (Chen et al., 2023), we adapt these techniques to predict
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relationships between datasets, treating dataset metadata as spatial features."
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- citation_id: https://w3id.org/heritage/citation/lokg-2024-cites-iswc2023
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citing_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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cited_work: https://w3id.org/heritage/publication/iswc-2023-best-paper
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citation_type: CITES_AS_EVIDENCE
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citation_intent: >-
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Cites spatial link prediction methods as evidence for geographic entity
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linking approaches used in LOKG.
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citation_context: >-
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"For geographic entity resolution and linking, we employ spatial embedding
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techniques similar to those proposed by Chen et al. (2023), combining
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coordinate-based distance metrics with semantic similarity."
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page_number: "28"
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- citation_id: https://w3id.org/heritage/citation/kg-2021-self-cite-intro
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citing_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_METADATA
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citation_intent: >-
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Internal cross-reference between survey sections for navigation.
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citation_context: >-
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"As discussed in Section 4.2, knowledge graph construction involves multiple
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stages from data extraction to schema alignment."
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page_number: "45"
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# Citations from Heritage-Linked Publications
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- citation_id: https://w3id.org/heritage/citation/brazilian-lokg-cites-lokg-2024
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citing_work: https://w3id.org/heritage/publication/lokg-brazilian-subset-2024
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cited_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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citation_type: EXTENDS
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citation_intent: >-
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Extends the LOKG framework by providing a comprehensive Brazilian cultural
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heritage subset with enhanced metadata and geographic coverage.
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citation_context: >-
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"Building upon the LOKG architecture described by Rossi and Meghini (2024),
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we present a specialized Brazilian subset that integrates 304 heritage institutions
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with enriched Portuguese-language metadata and linkage to Brazilian geographic
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identifiers from IBGE."
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page_number: "2"
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- citation_id: https://w3id.org/heritage/citation/dutch-consortium-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/dutch-glam-consortium-2023
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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References comprehensive knowledge graph survey to establish theoretical
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foundations for federated heritage knowledge graph architecture.
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citation_context: >-
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"Following the knowledge graph design patterns outlined by Hogan et al. (2021),
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the Dutch GLAM Consortium implements a federated architecture that preserves
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institutional autonomy while enabling unified semantic queries across heterogeneous
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collections."
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page_number: "4"
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- citation_id: https://w3id.org/heritage/citation/dutch-consortium-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/dutch-glam-consortium-2023
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: CITES_AS_EVIDENCE
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citation_intent: >-
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Cites Wikidata's collaborative editing model as evidence for community-driven
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metadata enrichment strategies in the consortium.
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citation_context: >-
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"The consortium adopts collaborative metadata enrichment strategies inspired
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by Wikidata's community-driven approach (Vrandečić and Krötzsch, 2014), enabling
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curators, researchers, and volunteers to contribute structured annotations
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within institutional governance frameworks."
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page_number: "12"
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- citation_id: https://w3id.org/heritage/citation/rembrandt-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/rijksmuseum-rembrandt-2024
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: USES_DATA_FROM
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citation_intent: >-
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Uses Wikidata as a source for Rembrandt biographical metadata, provenance
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information, and artwork identifiers to link analysis results.
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citation_context: >-
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"We enriched our dataset with Rembrandt biographical data and artwork provenance
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from Wikidata (Vrandečić and Krötzsch, 2014), enabling temporal correlation
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of brushwork patterns with documented life events and known commission dates."
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page_number: "8"
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- citation_id: https://w3id.org/heritage/citation/nha-cites-lokg-2024
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citing_work: https://w3id.org/heritage/publication/noord-hollands-archief-digital-2023
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cited_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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citation_type: DISCUSSES
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citation_intent: >-
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Discusses LOKG's metadata integration strategies in the context of archival
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digital transformation and cross-institutional discovery.
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citation_context: >-
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"Large-scale heritage knowledge graphs like LOKG (Rossi and Meghini, 2024)
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demonstrate the potential for unified discovery across institutional boundaries.
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Our digital transformation strategy positions the Noord-Hollands Archief to
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participate in similar federated heritage infrastructures."
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page_number: "24"
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- citation_id: https://w3id.org/heritage/citation/cms-study-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/collection-management-systems-2024
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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References Wikidata as an authoritative example of structured knowledge
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representation for entity reconciliation in library collection management systems.
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citation_context: >-
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"Many surveyed libraries now integrate Wikidata identifiers (Vrandečić and
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Krötzsch, 2014) into their collection management workflows for author
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disambiguation, subject heading reconciliation, and authority control,
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reflecting a broader trend toward Linked Open Data adoption."
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page_number: "18"
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- citation_id: https://w3id.org/heritage/citation/cms-study-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/collection-management-systems-2024
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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Cites knowledge graph design principles to analyze collection management
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system architectures and metadata modeling approaches.
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citation_context: >-
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"We evaluate each system's metadata architecture using the knowledge graph
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design patterns identified by Hogan et al. (2021), assessing schema expressiveness,
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query capabilities, and interoperability with external knowledge bases."
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page_number: "9"
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# ============================================================================
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# Citations from Diverse Heritage Publications (Books, Chapters, Reports, Preprints)
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# ============================================================================
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- citation_id: https://w3id.org/heritage/citation/linked-data-museums-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/linked-data-museums-2022
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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References comprehensive knowledge graph survey to establish theoretical
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foundations for museum Linked Data implementation.
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citation_context: >-
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"Following the knowledge graph design patterns outlined by Hogan et al. (2021),
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museums can structure collection metadata using established semantic web principles
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adapted for the unique requirements of cultural heritage objects and their complex
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provenance histories."
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page_number: "45"
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- citation_id: https://w3id.org/heritage/citation/linked-data-museums-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/linked-data-museums-2022
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: CITES_AS_EVIDENCE
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citation_intent: >-
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Uses Wikidata as a successful example of collaborative knowledge base construction
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for museums to follow.
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citation_context: >-
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"Wikidata (Vrandečić and Krötzsch, 2014) demonstrates how collaborative editing
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can build comprehensive art historical knowledge bases. Museums like the Van Gogh
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Museum now systematically align their collection records with Wikidata identifiers,
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enabling global discoverability and cross-institutional research."
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page_number: "127"
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- citation_id: https://w3id.org/heritage/citation/kb-3d-report-cites-lokg-2024
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citing_work: https://w3id.org/heritage/publication/kb-3d-digitization-report-2024
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cited_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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citation_type: DISCUSSES
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citation_intent: >-
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Discusses LOKG metadata aggregation strategies in the context of 3D digitization
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metadata sharing and preservation planning.
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citation_context: >-
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"Large-scale heritage knowledge graphs like LOKG (Rossi and Meghini, 2024) provide
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infrastructure for sharing rich metadata across institutions. Our 3D digitization
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workflow generates IIIF 3D manifests compatible with LOKG aggregation patterns,
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enabling future integration of volumetric heritage data."
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page_number: "72"
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- citation_id: https://w3id.org/heritage/citation/europeana-qa-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/europeana-aggregation-quality-2023
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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References knowledge graph quality assessment methods to inform Europeana
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metadata quality framework.
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citation_context: >-
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"Knowledge graph quality assessment (Hogan et al., 2021) encompasses syntactic
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validation, semantic consistency checking, and completeness metrics. We adapt
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these principles for cultural heritage metadata, where domain-specific quality
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dimensions include rights statement validity, multilingual completeness, and
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alignment with controlled vocabularies (AAT, TGN, ULAN)."
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page_number: "18"
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- citation_id: https://w3id.org/heritage/citation/europeana-qa-cites-lokg-2024
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citing_work: https://w3id.org/heritage/publication/europeana-aggregation-quality-2023
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cited_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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citation_type: CITES_AS_EVIDENCE
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citation_intent: >-
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Uses LOKG as evidence of successful heritage metadata aggregation requiring
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robust quality assessment.
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citation_context: >-
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"Recent heritage aggregation initiatives like LOKG (Rossi and Meghini, 2024)
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demonstrate that federated knowledge graphs require tiered quality assessment
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to balance inclusivity (accepting diverse metadata standards) with usability
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(ensuring minimum quality thresholds for search and discovery)."
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page_number: "35"
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- citation_id: https://w3id.org/heritage/citation/crowdsourcing-chapter-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/crowdsourcing-metadata-enrichment-2023
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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Cites Wikidata as authoritative model for crowdsourced cultural heritage metadata.
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citation_context: >-
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"Wikidata's collaborative editing model (Vrandečić and Krötzsch, 2014) provides
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a proven framework for museums implementing crowdsourcing initiatives. The
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Rijksmuseum Challenge adapted Wikidata's quality control mechanisms—including
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edit history tracking, volunteer reputation scoring, and curator review workflows—
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to balance volunteer autonomy with professional curatorial authority."
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page_number: "210"
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- citation_id: https://w3id.org/heritage/citation/arxiv-provenance-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/arxiv-gnn-provenance-2024
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: CITES_AS_AUTHORITY
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citation_intent: >-
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References knowledge graph construction methods for modeling artwork provenance
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as a graph structure.
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citation_context: >-
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"Art provenance networks are naturally represented as knowledge graphs (Hogan
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et al., 2021), where artworks, collectors, galleries, and auctions form entities
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connected by ownership, exhibition, and transaction relationships. Graph neural
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networks can exploit this structure to predict missing provenance links from
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incomplete historical records."
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page_number: "3"
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- citation_id: https://w3id.org/heritage/citation/arxiv-provenance-cites-wikidata-2018
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citing_work: https://w3id.org/heritage/publication/arxiv-gnn-provenance-2024
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cited_work: https://w3id.org/heritage/publication/jows-wikidata-2018
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citation_type: USES_DATA_FROM
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citation_intent: >-
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Uses Wikidata as training data source for graph neural network provenance model.
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citation_context: >-
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"We train our GNN model on 180,000 artworks with documented provenance from
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the Getty Provenance Index and Wikidata (Vrandečić and Krötzsch, 2014). Wikidata
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provides structured provenance chains (P127 'owned by', P195 'collection', P156
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'followed by') linking artworks to collectors, museums, and auction houses across
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centuries."
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page_number: "8"
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- citation_id: https://w3id.org/heritage/citation/llm-cataloging-preprint-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/osf-automated-cataloging-2024
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: DISCUSSES
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citation_intent: >-
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Discusses knowledge graph representation challenges in the context of LLM-generated
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museum metadata.
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citation_context: >-
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"Knowledge graphs enable museums to represent complex object relationships
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(Hogan et al., 2021), but LLM-generated metadata may produce taxonomic inconsistencies
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when classification schemas encode colonial power structures. We propose hybrid
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workflows where LLMs suggest metadata values constrained by culturally appropriate
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controlled vocabularies co-created with source communities."
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page_number: "15"
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- citation_id: https://w3id.org/heritage/citation/digital-preservation-book-cites-lokg-2024
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citing_work: https://w3id.org/heritage/publication/digital-preservation-handbook-2023
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cited_work: https://w3id.org/heritage/publication/tgdk-lokg-2024
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citation_type: CITES_AS_EVIDENCE
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citation_intent: >-
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Cites LOKG as evidence for importance of metadata aggregation in digital preservation
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planning.
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citation_context: >-
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"Federated heritage knowledge graphs like LOKG (Rossi and Meghini, 2024) enable
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coordinated digital preservation strategies across institutions. When preservation
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metadata (PREMIS events, fixity checks, format migrations) is aggregated via
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knowledge graphs, institutions can identify at-risk formats, share migration tools,
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and coordinate distributed preservation responsibilities."
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page_number: "328"
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- citation_id: https://w3id.org/heritage/citation/archival-appraisal-chapter-cites-kg-2021
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citing_work: https://w3id.org/heritage/publication/archival-appraisal-digital-age-2024
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cited_work: https://w3id.org/heritage/publication/swj-knowledge-graphs-2021
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citation_type: DISCUSSES
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citation_intent: >-
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Discusses knowledge graph representation of archival relationships to inform
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machine learning-assisted appraisal.
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citation_context: >-
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"Archival relationships (provenance, original order, custodial history) can be
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modeled as knowledge graphs (Hogan et al., 2021), enabling machine learning
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algorithms to learn contextual appraisal patterns. Noord-Hollands Archief's
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ML-assisted workflow represents records as graph nodes with archival context
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edges, allowing GNN models to propagate appraisal decisions through hierarchical
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fonds structures."
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page_number: "156"
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