Metamodeling is a general approach to expressing knowledge about classes and
properties in an ontology. It is a desirable modeling feature in multiple
applications that simplifies the extension and reuse of ontologies.
Nevertheless, allowing metamodeling without restrictions is problematic for
several reasons, mainly due to undecidability issues. Practical languages,
therefore, forbid classes to occur as instances of other classes or treat such
occurrences as semantically different objects. Specifically, meta-querying in
SPARQL under the Direct Semantic Entailment Regime (DSER) uses the latter
approach, thereby effectively not supporting meta-queries. However, several
extensions enabling different metamodeling features have been proposed over the
last decade. This paper deals with the Metamodeling Semantics (MS) over OWL 2
QL and the Metamodeling Semantic Entailment Regime (MSER), as proposed in
Lenzerini et al. (2015) and Lenzerini et al. (2020); Cima et al. (2017). A
reduction from OWL 2 QL to Datalog for meta-querying was proposed in Cima et
al. (2017). In this paper, we experiment with various logic programming tools
that support Datalog querying to determine their suitability as back-ends to
MSER query answering. These tools stem from different logic programming
paradigms (Prolog, pure Datalog, Answer Set Programming, Hybrid Knowledge
Bases). Our work shows that the Datalog approach to MSER querying is practical
also for sizeable ontologies with limited resources (time and memory). This
paper significantly extends Qureshi & Faber (2021) by a more detailed
experimental analysis and more background. Under consideration in Theory and
Practice of Logic Programming (TPLP).

本篇论文研究了基于 OWL 2 QL 和 Metamodeling Semantic Entailment Regime (MSER) 的 Metamodeling 语义，并使用不同的逻辑编程工具实验确定它们对 MSER 查询的适用性。