Regular Path Queries in Lightweight Description Logics: Complexity and Algorithms

Answering regular path queries in expressive Description Logics via alternating tree-automata

The high computational complexity of the expressive Description Logics (DLs) that underlie the OWL standard has motivated the study of their Horn fragments, which are usually tractable in data complexity and can also have lower combined complexity, particularly for query answering. In this paper we provide algorithms for answering conjunctive 2-way regular path queries (2CRPQs), a non-trivial generalization of plain conjunctive queries, in the Horn fragments of the DLs SHOIQ and SROIQ underlying OWL 1 and OWL 2. We show that the combined complexity of the problem is ExpTime-complete for Horn-SHOIQ and 2ExpTime- complete for the more expressive Horn-SROIQ, but is PTime-complete in data complexity for both. In contrast, even decidability of plain conjunctive queries is still open for full SHOIQ and SROIQ. These are the first completeness results for query answering in DLs with inverses, nominals, and counting, and show that for the considered logics the problem is not more expensive than standard reasoning.

The basic querying mechanism over semistructured data, namely regular path queries, asks for all pairs of objects that are connected by a path conforming to a regular expression. We consider conjunctive two-way regular path queries (C2RPQc’s), which extend regular path queries with two features. First, they add the inverse operator, which allows for expressing navigations in the database that traverse the edges both backward and forward. Second, they allow for using conjunctions of atoms, where each atom specifies that a regular path query with inverse holds between two terms, where each term is either a variable or a constant. For such queries we address the problem of view-based query answering, which amounts to computing the result of a query only on the basis of a set of views. More specifically, we present the following results: (1) We exhibit a mutual reduction between query containment and the recognition problem for view-based query answering for C2RPQc’s, i.e., checking whether a given tuple is in the certain answer to a query. Based on such a result, we can show that the problem of view-based query answering for C2RPQc’s is EXPSPACE-complete. (2) By exploiting techniques based on alternating two-way automata we show that for the restricted class of tree two-way regular path queries (in which the links between variables form a tree), query containment and view-based query answering are, rather surprisingly, in PSPACE (and hence, PSPACE-complete). (3) We present a technique to obtain view-based query answering algorithms that compute the whole set of tuples in the certain answer, instead of requiring to check each tuple separately. The technique is parametric wrt the query language, and can be applied both to C2RPQc’s and to tree-queries.KeywordsRegular ExpressionQuery LanguageRegular LanguageConjunctive QueryTree AutomatonThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Ontologies based on Description Logic (DL) represent general background knowledge in a terminology (TBox) and the actual data in an ABox. DL systems can then be used to compute consequences (such as answers to certain queries) from an ontology consisting of a TBox and an ABox. Since both human-made and machine-learned data sets may contain errors, which manifest themselves as unintuitive or obviously incorrect consequences, repairing DL-based ontologies in the sense of removing such unwanted consequences is an important topic in DL research. Most of the repair approaches described in the literature produce repairs that are not optimal, in the sense that they do not guarantee that only a minimal set of consequences is removed. In a series of papers, we have developed an approach for computing optimal repairs, starting with the restricted setting of an EL instance store, extending this to the more general setting of a quantified ABox (where some individuals may be anonymous), and then adding a static EL TBox. Here, we extend the expressivity of the underlying DL considerably, by adding nominals, inverse roles, regular role inclusions and the bottom concept to EL, which yields a fragment of the well-known DL Horn-SROIQ. The ideas underlying our repair approach still apply to this DL, though several non-trivial extensions are needed to deal with the new constructors and axioms. The developed repair approach can also be used to treat unwanted consequences expressed by certain conjunctive queries or regular path queries, and to handle Horn-ALCOI TBoxes with regular role inclusions.

An important issue that arises when querying description logic (DL) knowledge bases is how to handle the case in which the knowledge base is inconsistent. Indeed, while it may be reasonable to assume that the TBox (ontology) has been properly debugged, the ABox (data) will typically be very large and subject to frequent modifications, both of which make errors likely. As standard DL semantics is useless in such circumstances (everything is entailed from a contradiction), several alternative inconsistency-tolerant semantics have been proposed with the aim of providing meaningful answers to queries in the presence of such data inconsistencies. In the first part of this chapter, we present and compare these inconsistency-tolerant semantics, which can be applied to any DL (or ontology language). The second half of the chapter summarizes what is known about the computational properties of these semantics and gives an overview of the main algorithmic techniques and existing systems, focusing on DLs of the DL-Lite family.

In this paper, we introduce preferential regular path queries. These are regular path queries whose symbols are annotated with preference weights for “scaling” up or down the intrinsic importance of matching a symbol against a (semistructured) database edge label. Annotated regular path queries are expressed syntactically as annotated regular expressions. We interpret these expressions in a uniform semiring framework, which allows different semantics specializations for the same syntactic annotations. For our preference queries, we study three important aspects: (1) (progressive) query answering (2) (certain) query answering in LAV data-integration systems, and (3) query containment and equivalence. In all of these, we obtain important positive results, which encourage the use of our preference framework for enhanced querying of semistructured databases.KeywordsRegular ExpressionQuery AnswerQuery AnsweringSemistructured DataPreference SemanticThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Similarity-based relaxed instance queries

Graph databases in many applications - semantic web, transport or biological networks among others - are not only large, but also frequently modified. Evaluating graph queries in this dynamic context is a challenging task, as those queries often combine first-order and navigational features. Motivated by recent results on maintaining dynamic reachability, we study the dynamic evaluation of traditional query languages for graphs in the descriptive complexity framework. Our focus is on maintaining regular path queries, and extensions thereof, by first-order formulas. In particular we are interested in path queries defined by non-regular languages and in extended conjunctive regular path queries (which allow to compare labels of paths based on word relations). Further we study the closely related problems of maintaining distances in graphs and reachability in product graphs. In this preliminary study we obtain upper bounds for those problems in restricted settings, such as undirected and acyclic graphs, or under insertions only, and negative results regarding quantifier-free update formulas. In addition we point out interesting directions for further research.

The resilience problem for a query and an input set or bag database is to compute the minimum number of facts to remove from the database to make the query false. In this paper, we study how to compute the resilience of Regular Path Queries (RPQs) over graph databases. Our goal is to characterize the regular languages L for which it is tractable to compute the resilience of the existentially-quantified RPQ built from L . We show that computing the resilience in this sense is tractable (even in combined complexity) for all RPQs defined from so-called local languages . By contrast, we show hardness in data complexity for RPQs defined from the following language classes (after reducing the languages to eliminate redundant words): all finite languages featuring a word containing a repeated letter, and all languages featuring a specific kind of counterexample to being local (which we call four-legged languages). The latter include in particular all languages that are not star-free . Our results also imply hardness for all non-local languages with a so-called neutral letter . We last highlight some remaining obstacles towards a full dichotomy.

Revising knowledge bases (KBs) in description logics (DLs) in a syntax-independent manner is an important, nontrivial problem for the ontology management and DL communities. Several attempts have been made to adapt classical model-based belief revision and update techniques to DLs, but they are restricted in several ways. In particular, they do not provide operators or algorithms for general DL KB revision. The key difficulty is that, unlike propositional logic, a DL KB may have infinitely many models with complex (and possibly infinite) structures, making it difficult to define and compute revisions in terms of models. In this paper, we study general KBs in a specific DL in the DL-Lite family. We introduce the concept of features for such KBs, develop an alternative semantic characterization of KBs using features (instead of models), define two specific revision operators for KBs, and present the first algorithm for computing best approximations for syntax-independent revisions of KBs.

We characterize the expressive power of description logic (DL) TBoxes, both for expressive DLs such as ALC and ALCQIO and lightweight DLs such as DL-Lite and EL. Our characterizations are relative to first-order logic, based on a wide range of semantic notions such as bisimulation, equisimulation, disjoint union, and direct product. We exemplify the use of the characterizations by a first study of the following novel family of decision problems: given a TBox T formulated in a DL L, decide whether T can be equivalently rewritten as a TBox in the fragment L′ of L.

In this paper we present a weak and a strong intuitionistic calculus for query answering in Description Logics (DL). Given the standard model-theoretic semantics for DL, a complete query-answering calculus has to perform complex case analyses to cope with implicit disjunctions stemming from some of the concept-forming operators in DL. To avoid this complexity we propose an intuitionistic approach to query answering based on the Sequent-Calculus-style axiomatization of DL we have developed in [20] and [21]. By taking into account only the intuitionistic inference schemata of this axiomatization, we obtain a strong intuitionistic query-answering calculus. An additional restriction to reasoning about explicit objects allows a further simplification of the proof theory and yields a weak intuitionistic calculus.We prove completeness of these calculi wrt axiomatic semantics based on the Intuitionistic Sequent Calculus. For the weak calculus we also give a least fixed point semantics as known from Deductive Databases and Logic Programming.KeywordsDescription LogicsIntuitionistic Sequent CalculusLeast Fixed Point SemanticsQuery Answering

Description logics (DLs) and rule-based systems are two prominent families of knowledge representation formalisms. While DLs are focused on describing and reasoning about conceptual knowledge, rules are focused on answering queries about facts in the knowledge base. So far, research on DLs has been largely isolated from the research on rules. With the advent of the Semantic Web, however, it became apparent that neither formalism alone can cover all the practical use cases. The integration between DLs and rules, however, is technically challenging due to significant differences in the underlying semantic assumptions. In particular, DLs are based on standard first-order semantics and open-world assumption, whereas rules typically employ closed-world semantics based on a variant of circumscription. In my talk, I shall present an overview of the benefits of integrating DLs and rules in a coherent semantic framework, and shall discuss the main challenges in achieving a tight integration. I shall present an overview of the approaches currently discussed in literature. Finally, I shall present in more detail the approach that is based on the nonmonotonic logic MKNF by Lifschitz. This approach is tight in the sense that DLs and rules are given a common model-theoretic semantics. Furthermore, it is faithful in the sense that it is compatible with the original semantics of both DLs and rules. Finally, it is expressive and can naturally capture many of the existing proposals. I shall discuss the definitions of the semantics of the hybrid formalism, present a decision procedure for a particular decidable fragment, and discuss the complexity bounds of reasoning.

A regular path query (RPQ) is a regular expression q that returns all node pairs (u, v) from a graph database that are connected by an arbitrary path labelled with a word from L(q). The obvious algorithmic approach to RPQ-evaluation (called PG-approach), i.e., constructing the product graph between an NFA for q and the graph database, is appealing due to its simplicity and also leads to efficient algorithms. However, it is unclear whether the PG-approach is optimal. We address this question by thoroughly investigating which upper complexity bounds can be achieved by the PG-approach, and we complement these with conditional lower bounds (in the sense of the fine-grained complexity framework). A special focus is put on enumeration and delay bounds, as well as the data complexity perspective. A main insight is that we can achieve optimal (or near optimal) algorithms with the PG-approach, but the delay for enumeration is rather high (linear in the database). We explore three successful approaches towards enumeration with sub-linear delay: super-linear preprocessing, approximations of the solution sets, and restricted classes of RPQs.

Horn formulae are widely used in different settings that include logic programming, answer set programming, description logics, deductive databases, and system verification, among many others. One concrete example is concept subsumption in lightweight description logics, which can be reduced to inference in propositional Horn formulae. Some problems require one to reason with inconsistent Horn formulae. This is the case when providing minimal explanations of inconsistency. This paper proposes efficient algorithms for a number of decision, function and enumeration problems related with inconsistent Horn formulae. Concretely, the paper develops efficient algorithms for finding and enumerating minimal unsatisfiable subsets (MUSes), minimal correction subsets (MCSes), but also for computing the lean kernel. The paper also shows the practical importance of some of the proposed algorithms.