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).

翻译：元建模是一种在本体中表达关于类和属性知识的一般性方法。它是在多种应用中所需的建模特性，可简化本体的扩展与复用。然而，不加限制地允许元建模会带来问题，主要原因在于可判定性难题。因此，实用语言禁止将类用作其他类的实例，或将此类用法视为语义上不同的对象。具体而言，在直接语义蕴含机制下的SPARQL元查询采用了后一种方法，实际上不支持元查询。然而，过去十年间，已提出了若干支持不同元建模特性的扩展。本文处理了Lenzerini等人（2015）、Lenzerini等人（2020）及Cima等人（2017）提出的OWL 2 QL上的元建模语义与元建模语义蕴含机制。Cima等人（2017）提出了从OWL 2 QL到Datalog的元查询归约方法。本文实验了多种支持Datalog查询的逻辑编程工具，以确定其作为MSER查询回答后端的适用性。这些工具源自不同的逻辑编程范式（Prolog、纯Datalog、回答集编程、混合知识库）。实验表明，基于Datalog的MSER查询方法对资源（时间和内存）有限的较大本体也具有实用性。本文在Qureshi & Faber（2021）基础上进行了显著扩展，提供了更详细的实验分析和背景介绍。本文正在考虑发表于《逻辑编程理论与实践》。