Ruben Verborgh’s data

Matches in Ruben’s data for { <https://ruben.verborgh.org/publications/tam_swj_2026/#publication> ?p ?o }

• publication author me.
• publication author me.
• publication author bryan_elliott_tam.
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• publication about query_processing.
• publication about interoperability.
• publication about Solid.
• publication about Social_media.
• publication about Metadata.
• publication about RDF.
• publication author me.
• publication author me.
• publication author bryan_elliott_tam.
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• publication type ScholarlyArticle.
• publication type Article.
• publication type Document.
• publication type Q386724.
• publication type CreativeWork.
• publication type Document.
• publication type Work.
• publication type Document.
• publication P50 me.
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• publication maker me.
• publication title "Traveling with a Map: Reducing the Search Space of Link Traversal Queries Using RDF Shapes".
• publication isPartOf semantic_web_journal.
• publication name "Traveling with a Map: Reducing the Search Space of Link Traversal Queries Using RDF Shapes".
• publication label "Traveling with a Map: Reducing the Search Space of Link Traversal Queries Using RDF Shapes".
• publication name "Traveling with a Map: Reducing the Search Space of Link Traversal Queries Using RDF Shapes".
• publication topic query_processing.
• publication topic interoperability.
• publication topic Solid.
• publication topic Social_media.
• publication topic Metadata.
• publication topic RDF.
• publication subject query_processing.
• publication subject interoperability.
• publication subject Solid.
• publication subject Social_media.
• publication subject Metadata.
• publication subject RDF.
• publication authorList b0_b1876.
• publication topic query_processing.
• publication topic interoperability.
• publication topic Solid.
• publication topic Social_media.
• publication topic Metadata.
• publication topic RDF.
• publication abstract "The centralization of web information raises legal and ethical concerns, particularly in social, healthcare, and education applications. Decentralized architectures offer a promising alternative by keeping data closer to its source, yet efficient query processing remains a significant challenge. Link Traversal Query Processing (LTQP) enables querying across decentralized networks, however, it often suffers from long execution times and high data transfer costs due to the large number of Hypertext Transfer Protocol (HTTP) requests involved. In many scenarios, queries are highly selective with respect to the data model objects distributed across the network. For example, in a social media application where users store heterogeneous data, a query may focus solely on the posts and comments created by users, without requiring any of their additional user information. We refer to such queries as data-model selective. We propose a shape-based pruning approach that relies on shape indexes and a query-shape subsumption algorithm to reduce the search space and, consequently, the number of HTTP requests for such queries. We formalize this approach as a link pruning mechanism for LTQP and evaluate its effectiveness on social media queries using the SolidBench benchmark across multiple evaluation metrics. Our results show that shape-based pruning substantially improves query execution time, first-result arrival time, diefficiency, and network usage for data-model selective queries, while having insignificant impact on non-selective data-model queries. These gains come at the cost of only a minor increase in the number of triples per shape-index instance. Moreover, our approach is resilient, retaining performance benefits even in networks where some data providers do not supply shape-index information. This work demonstrates that shape-based metadata can significantly optimize LTQP in decentralized knowledge graphs for an important class of queries. By exposing such metadata, data providers not only enhance data quality and interoperability but also improve the efficiency of traversal-based query processing.".
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