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KI - Künstliche Intelligenz
Erschienen in: KI - Künstliche Intelligenz 2/2016

01.12.2015 | Technical Contribution

Is Your Database System a Semantic Web Reasoner?

verfasst von: Markus Krötzsch, Sebastian Rudolph

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 2/2016

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Abstract

Databases and semantic technologies are an excellent match in scenarios requiring the management of heterogeneous or incomplete data. In ontology-based query answering, application knowledge is expressed in ontologies and used for providing better query answers. This enhancement of database technology with logical reasoning remains challenging—performance is critical. Current implementations use time-consuming pre-processing to materialise logical consequences or, alternatively, compute a large number of large queries to be answered by a database management system (DBMS). Recent research has revealed a third option using recursive query languages to “implement” ontological reasoning in DBMS. For lightweight ontology languages, this is possible using the popular Semantic Web query language SPARQL 1.1, other cases require more powerful query languages like Datalog, which is also seeing a renaissance in DBMS today. Herein, we give an overview of these areas with a focus on recent trends and results.
Vorheriger Artikel Extracting Semantics from Unconstrained Navigation on Wikipedia
Nächster Artikel Abductive Conjunctive Query Answering w.r.t. Ontologies

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Fußnoten
1
We use ? for variables in triple patterns like in SPARQL queries.
 
2
In this terminology, materialisation would correspond to forward chaining.
 
3
Like always in OBQA, queries are evaluated under set semantics, i.e., results cannot contain duplicates. The semantics of ontological reasoning under bag (multiset) semantics is not usually considered, and would most likely be hard or impossible to implement in many cases.
 
4
Using unions of queries, one could also express the rewritten queries in traditional query rewriting as a single query, but this query would be exponentially large. Issuing many small queries is less likely to overwhelm the DBMS, and is therefore preferable in this case.
 
5
More precisely, this approach can only be employed for formalisms where the data complexity of OBQA is in the complexity class \(\textsc {AC}^0\), and hence strictly below logarithmic space.
 
6
DFG Sonderforschungsbereich 912: Highly Adaptive Energy-Efficient Computing.
 
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Metadaten
Titel
Is Your Database System a Semantic Web Reasoner?
verfasst von
Markus Krötzsch
Sebastian Rudolph
Publikationsdatum
01.12.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 2/2016
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-015-0412-x

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