Prime examples include mediator systems that provide access to multiple information sources, data mining and archeology, mobile databases, data warehouses, and decision support systems. Furthermore, some database vendors are considering the maintenance of materialized views also as a means for query optimization. As a result, problems concerning materialized views have recently received a lot of attention in the database community. A view is essentially a query. If the answers to the query are physically maintained, the view is said to be materialized.
Horn rule languages have formed the basis for many Artificial Intelligence application languages, but are not expressive enough to model domains with a rich hierarchical structure. Description logics have been designed especially to model rich hierarchies. Several applications would significantly benefit from combining the expressive power of both formalisms. This paper focuses on combining recursive function-free Horn rules with the expressive description logic &,Chf'R, and shows exactly when a hybrid language with decidable inference can be obtained. First, we show that several of the core constructors of description logics lead by themselves to undecidability of inference when combined with recursive function-free Horn rules. We then show that without these constructors we obtain a maximal subset of & CN7E that yields a decidable hybrid language. Finally, we describe a restriction on the Horn rules that guarantees decidable inference when combined with all of & Cn/Z, and covers many of the common usages of recursive rules.
This problem is relevant in several fields, such as information integration, query optimization, and data warehousing, and has been studied recently in different settings. In this paper we address answering queries using views in a setting where intensional knowledge about the domain is represented using a very expressive Description Logic equipped with nary relations, and queries are nonrecursive datalog queries whose predicates are the concepts and relations that appear in the Description Logic knowledge base. We study the problem under different assumptions, namely, closed and open domain, and sound, complete, and exact information on view extensions. We show that under the closed domain assumption, in which the set of all objects in the knowledge base coincides with the set of objects stored in the views, answering queries using views is already intractable. We show also that under the open domain assumption the problem is decidable in double exponential time.
The DL-Lite family of Description Logics has been designed with the specific goal of allowing for answering complex queries (in particular, conjunctive queries) over ontologies with very large instance sets (ABoxes). So far, in DL-Lite systems, this goal has been actually achieved only for relatively simple (short) conjunctive queries. In this paper we present Presto, a new query answering technique for DL-Lite ontologies, and an experimental comparison of Presto with the main previous approaches to query answering in DL-Lite. In practice, our experiments show that, in real ontologies, current techniques are only able to answer conjunctive queries of less than 7-10 atoms (depending on the complexity of the TBox), while Presto is actually able to handle conjunctive queries of up to 30 atoms. Furthermore, in the cases that are already successfully handled by previous approaches, Presto is significantly more efficient.
The integration of Description Logics and Datalog rules presents many semantic and computational problems. In particular, reasoning in a system fully integrating Description Logics knowledge bases (DL-KBs) and Datalog programs is undecidable. Many proposals have overcomed this problem through a "safeness" condition that limits the interaction between the DL-KB and the Datalog rules. Such a safe integration of Description Logics and Datalog provides for systems with decidable reasoning, at the price of a strong limitation in terms of expressive power. In this paper we define DL log, a general framework for the integration of Description Logics and disjunctive Datalog. From the knowledge representation viewpoint, DL log extends previous proposals, since it allows for a tighter form of integration between DL-KBs and Datalog rules which overcomes the main representational limits of the approaches based on the safeness condition. From the reasoning viewpoint, we present algorithms for reasoning in DL log, and prove decidability and complexity of reasoning in DL log for several Description Logics. To the best of our knowledge, DL log constitutes the most powerful decidable combination of Description Logics and disjunctive Datalog rules proposed so far.