Type-based computation of knowledge graph statistics

Savnik, Iztok (avtor), Nitta, Kiyoshi (avtor), Škrekovski, Riste (avtor), Augsten, Nikolaus (avtor)

We propose a formal model of a knowledge graph (abbr. KG) that classifies the ground triples into sets that correspond to the triple types. The triple types are partially ordered by the sub-type relation. Consequently, the sets of ground triples that are the interpretations of triple types are partially ordered by the subsumption relation. The types of triple patterns restrict the sets of ground triples, which need to be addressed in the evaluation of triple patterns, to the interpretation of the types of triple patterns. Therefore, a schema graph of a KG should include all triple types that are likely to be determined as the types of triple patterns. The stored schema graph consists of the selected triple types that are stored in a KG and the complete schema graph includes all valid triple types of KG. We propose choosing the schema graph, which consists of the triple types from a strip around the stored schema graph, i.e., the triple types from the stored schema graph and some adjacent levels of triple types with respect to the sub-type relation. Given a selected schema graph, the statistics are updated for each ground triple t from a KG. First, we determine the set of triple types stt from the schema graph that are affected by adding a triple t to an RDF store. Finally, the statistics of triple types from the set stt are updated.

• Jezik: Angleški jezik
• Ključne besede: knowledge graphs, RDF stores, graph database systems, graph databases, database statistics
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: Str. 787-815
• Številčenje: Vol. 93, iss. 5
• PID: 20.500.12556/RUL-178129
• UDK: 004.65
• ISSN pri članku: 1012-2443
• DOI: 10.1007/s10472-024-09965-3
• COBISS.SI-ID: 223651843
• Datum objave v RUL: 19.01.2026

Gradivo je del revije

• Naslov: Annals of mathematics and artificial intelligence
• Skrajšan naslov: Ann. math. artif. intell.
• Založnik: Springer Nature
• ISSN: 1012-2443
• COBISS.SI-ID: 43126017

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: grafi znanja, RDF zbirke podatkov, grafovske podatkovne baze

Projekti

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P1-0383
• Naslov: Kompleksna omrežja

• Financer: Federal State of Salzburg
• Številka projekta: 20102-F2101143-FPR
• Naslov: Digital Neuroscience Initiative