Counting Query Answers over a DL-Lite Knowledge Base (extended version)

@article{Calvanese2020CountingQA,
  title={Counting Query Answers over a DL-Lite Knowledge Base (extended version)},
  author={Diego Calvanese and Julien Corman and Davide Lanti and Simon Razniewski},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.05886}
}
Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a… 

Figures and Tables from this paper

Counting Query Answers over a DL-Lite Knowledge Base
TLDR
This paper focuses on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration, in the context of OntologyMediated Query Answering/Ontology-based Data Access (OMQA/OBDA).
Rewriting Count Queries over DL-Lite TBoxes with Number Restrictions
TLDR
The algorithm supports number restrictions on the RHS of axioms in the input TBox, which can be used to encode statistics in the binary encoding of these numbers.
Cardinality Queries over DL-Lite Ontologies
TLDR
It is proved that cardinality query answering is tractable (TC) in data complexity when the ontology is formulated in DL-Litecore, but the problem becomes coNP-hard as soon as role inclusions are allowed.

References

SHOWING 1-10 OF 35 REFERENCES
Counting Query Answers over a DL-Lite Knowledge Base
TLDR
This paper focuses on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration, in the context of OntologyMediated Query Answering/Ontology-based Data Access (OMQA/OBDA).
Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family
TLDR
It is shown that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in thesize of the ABox, which is the first result ofPolynomial-time data complexity for query answering over DL knowledge bases.
Ontology-Mediated Query Answering with Data-Tractable Description Logics
TLDR
A brief introduction to ontology-mediated query answering using description logic (DL) ontologies, with a focus on DLs for which query answering scales polynomially in the size of the data, as these are best suited for applications requiring large amounts of data.
Bag Semantics of DL-Lite with Functionality Axioms
TLDR
Two variants of bag semantics for query answering over DL-Lite with functional roles are studied, extending basic \(\textit{DL- lite} _{\mathcal{F}}\), extending basic (_{\textit {core}}\) withfunctional roles.
On the Parametrised Complexity of Tree-Shaped Ontology-Mediated Queries in OWL 2 QL
TLDR
An ontology T is constructed such that answering OMQs with tree-shaped CQs q is W[1]-hard if the number of leaves in q is regarded as the parameter, showing that treating it as a parameter does not make the problem fixed-parameter tractable, even for a fixed ontology.
Query Rewriting in DL-Lite_^(HN)_horn
In this paper we present practical algorithms for query answering and knowledge base satisfiability checking in DL-Lite ) horn , a logic from the extended DL-Lite family that contains horn concept
Query Languages for Bags and Aggregate Functions
TLDR
Theoretical foundations for querying databases based on bags, and the expressive power of BQL and related languages, are investigated in depth and it is proved that these languages possess the conservative extension property.
...
1
2
3
4
...