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Naslov:Type-based computation of knowledge graph statistics
Avtorji:ID Savnik, Iztok (Avtor)
ID Nitta, Kiyoshi (Avtor)
ID Škrekovski, Riste (Avtor)
ID Augsten, Nikolaus (Avtor)
Datoteke:URL https://link.springer.com/article/10.1007/s10472-024-09965-3
 
.pdf s10472-024-09965-3.pdf (561,96 KB)
MD5: FB60E3D4F9D677536AA89991E3A8801D
 
Jezik:Angleški jezik
Vrsta gradiva:Neznano
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FIŠ - Fakulteta za informacijske študije v Novem mestu
Opis: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.
Ključne besede:knowledge graphs, RDF stores, graph database systems
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Datum sprejetja članka:24.12.2024
Datum objave:17.01.2025
Leto izida:2025
Št. strani:str. 1-29
Številčenje:Vol. 93, no. [early view]
PID:20.500.12556/ReVIS-11727 Novo okno
UDK:004.65
ISSN pri članku:1012-2443
COBISS.SI-ID:223651843 Novo okno
DOI:10.1007/s10472-024-09965-3 Novo okno
Datum objave v ReVIS:02.06.2025
Število ogledov:99
Število prenosov:0
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:Annals of mathematics and artificial intelligence
Skrajšan naslov:Ann. math. artif. intell.
Založnik:J.C. Baltzer AG
ISSN:1012-2443
COBISS.SI-ID:43126017 Novo okno

Gradivo je financirano iz projekta

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P1-0383
Naslov:Kompleksna omrežja

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

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


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