A Review of Answering Queries over Ontologies Based on Databases

Introduction

The integration of databases and ontology-based systems became an important research problem for the Semantic Web and database communities. On one hand, the databases may be enriched by an ontological theory that enforces expressive constraints over the databases. Such constraints go far beyond traditional integrity constraints and can be used to enable complex reasoning tasks over the database instances (Gottlob, Orsi, & Pieris, 2011). On the other hand, the database research community has successfully developed a wide theory corpus and a mature and efficient technology to deal with large and persistent amounts of information. In this case, some mature database techniques (e.g., data management, maintenance, and query) may be employed to handle some issues of ontologies, such as storage and query of ontologies. To investigate these issues and more importantly serve as helping non-experts grasp the main ideas and results of querying ontologies with databases and identifying the direction of querying ontologies for the Semantic Web study, in this chapter, we aim at providing a review of answering queries over ontologies with databases.

In particular, one of the main tasks in databases and ontologies remains that of query answering. It is well-known that query is one of the things that make databases so powerful, and the related techniques of database queries have been investigated for many years. Also, querying is the fundamental mechanism for extracting information from a knowledge base. In the Semantic Web, the ontology layer is highly important, and has led to a vast corpus of literature. The ontology can provide a high-level conceptual view of the data repository, and specify implicit concepts and roles that extend the vocabulary of the database with terms that are relevant for specific applications (Ortiz & Simkus, 2012). When clients access the application ontology, the ontology can be exploited to access the data and to answer queries taking into account the knowledge that is implicit in the ontology. To this end, reasoning is of paramount importance when querying the implicit knowledge in the ontology. As we have known, description logics (DLs for short) (Baader, Calvanese, & McGuinness, 2003), as the logic foundation of the World Wide Web Consortium (W3C) recommendation Web Ontology Language (OWL), have a long tradition in knowledge representation and reasoning and play a central role in ontology reasoning. In these contexts, many works concerning query answering in ontologies refer in fact to DLs, which led to wide studies of answering queries over DL knowledge bases (KBs). Considering the different expressivity of the DLs and the trade-off between the expressivity of DLs and the complexity of their querying and reasoning problems, many query works focus on several different families of DLs, to name a few, the lightweight DLs of the DL-Lite (Calvanese et al., 2013, 2007) and ℰℒ (Baader, Brandt, & Lutz, 2005) families, and the families of expressive DLs (Ortiz, 2010). The references (Ortiz & Simkus, 2012) and (Ortiz, 2013) survey some query answering techniques for both lightweight and expressive DLs, and give an overview of the computational complexity landscape.