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Title:Type-based computation of knowledge graph statistics
Authors:ID Savnik, Iztok (Author)
ID Nitta, Kiyoshi (Author)
ID Škrekovski, Riste (Author)
ID Augsten, Nikolaus (Author)
Files:URL https://link.springer.com/article/10.1007/s10472-024-09965-3
 
.pdf s10472-024-09965-3.pdf (561,96 KB)
MD5: FB60E3D4F9D677536AA89991E3A8801D
 
Language:English
Work type:Unknown
Typology:1.01 - Original Scientific Article
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract: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.
Keywords:knowledge graphs, RDF stores, graph database systems
Publication status:Published
Publication version:Version of Record
Article acceptance date:24.12.2024
Publication date:17.01.2025
Year of publishing:2025
Number of pages:str. 1-29
Numbering:Vol. 93, no. [early view]
PID:20.500.12556/ReVIS-11727 New window
COBISS.SI-ID:223651843 New window
UDC:004.65
ISSN on article:1012-2443
DOI:10.1007/s10472-024-09965-3 New window
Publication date in ReVIS:02.06.2025
Views:492
Downloads:4
Metadata:XML DC-XML DC-RDF
:
SAVNIK, Iztok, NITTA, Kiyoshi, ŠKREKOVSKI, Riste and AUGSTEN, Nikolaus, 2025, Type-based computation of knowledge graph statistics. Annals of mathematics and artificial intelligence [online]. 2025. Vol. 93, no. early view, p. 1–29. [Accessed 16 November 2025]. DOI 10.1007/s10472-024-09965-3. Retrieved from: https://revis.openscience.si/IzpisGradiva.php?lang=eng&id=11727
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Record is a part of a journal

Title:Annals of mathematics and artificial intelligence
Shortened title:Ann. math. artif. intell.
Publisher:J.C. Baltzer AG
ISSN:1012-2443
COBISS.SI-ID:43126017 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P1-0383
Name:Kompleksna omrežja
Funder:Federal State of Salzburg
Project number:20102-F2101143-FPR
Name:Digital Neuroscience Initiative

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:grafi znanja, RDF zbirke podatkov, grafovske podatkovne baze


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