We formalise a variant of Datalog that allows complex values constructed by nesting elements of the input database in sets and tuples. We study its complexity and give a translation into sets of tuple-generating dependencies (TGDs) for which the standard chase terminates on any input database. We identify a fragment for which reasoning is tractable. As membership is undecidable for this fragment, we develop decidable sufficient conditions. 2012 ACM Subject Classification Theory of computation → Complexity theory and logic; Theory of computation → Constraint and logic programming; Theory of computation → Logic and databases Acknowledgements We would like to thank the anonymous reviewers for their detailed feedback that led to this revised version of the paper.
We formalise a variant of Datalog that allows complex values constructed by nesting elements of the input database in sets and tuples. We study its complexity and give a translation into sets of tuple-generating dependencies (TGDs) for which the standard chase terminates on any input database. We identify a fragment for which reasoning is tractable. As membership is undecidable for this fragment, we develop decidable sufficient conditions. 2012 ACM Subject Classification Theory of computation → Complexity theory and logic; Theory of computation → Constraint and logic programming; Theory of computation → Logic and databases Acknowledgements We would like to thank the anonymous reviewers for their detailed feedback that led to this revised version of the paper.
Data exchange, the problem of transferring data from a source schema to a target schema, has been studied for several years. The semantics of answering positive queries over the target schema has been defined in early work, but little attention has been paid to more general queries. A few proposals of semantics for more general queries exist but they either do not properly extend the standard semantics under positive queries, giving rise to counterintuitive answers, or they make query answering undecidable even for the most important data exchange settings, for example, with weakly-acyclic dependencies. The goal of this paper is to provide a new semantics for data exchange that is able to deal with general queries. At the same time, we want our semantics to coincide with the classical one when focusing on positive queries, and to not trade-off too much in terms of complexity of query answering. We show that query answering is undecidable in general under the new semantics, but it is $\text{co}\text{NP}\text{-complete}$ when the dependencies are weakly-acyclic. Moreover, in the latter case, we show that exact answers under our semantics can be computed by means of logic programs with choice, thus exploiting existing efficient systems. For more efficient computations, we also show that our semantics allows for the construction of a representative target instance, similar in spirit to a universal solution, that can be exploited for computing approximate answers in polynomial time.
Existential rules form an expressive ${{\textsf{Datalog}}}$ -based language to specify ontological knowledge. The presence of existential quantification in rule-heads, however, makes the main reasoning tasks undecidable. To overcome this limitation, in the last two decades, a number of classes of existential rules guaranteeing the decidability of query answering have been proposed. Unfortunately, only some of these classes fully encompass ${{\textsf{Datalog}}}$ and, often, this comes at the price of higher computational complexity. Moreover, expressive classes are typically unable to exploit tools developed for classes exhibiting lower expressiveness. To mitigate these shortcomings, this paper introduces a novel general syntactic condition that allows us to define, systematically and in a uniform way, from any decidable class $\mathcal{C}$ of existential rules, a new class called ${{\textsf{Dyadic-}\mathcal{C}}}$ enjoying the following properties: ( i ) it is decidable; ( ii ) it generalizes ${{\textsf{Datalog}}}$ ; ( iii ) it generalizes $\mathcal{C}$ ; ( iv ) it can effectively exploit any reasoner for query answering over $\mathcal{C}$ ; and ( v ) its computational complexity does not exceed the highest between the one of $\mathcal{C}$ and the one of ${{\textsf{Datalog}}}$ .
This paper describes an intelligent ecosystem that can continuously monitor patients’ health conditions, whether at home, at work or during recreational activities, by leveraging a creative blend of wearable medical devices, intelligent agents (IA) and complex event processing (CEP). With the help of a smart application, linking wearable devices and the power of IA and CEP, patients will be constantly and actively supervised during their daily activities. This can even save their lives in case they experience sudden or gradual problems. Thanks to our system, patients with chronic illnesses that are not serious but potentially unstable will no longer overburden first aid services. This is also helpful in containing the spread of COVID-19. Specifically, in this paper, we focus on automatic monitoring of vital parameters, electrocardiogram analysis and psoriasis detection. Experimental results conducted on real patients show how promising our approach is.
We illustrate how Flix can be used as a powerful and expressive meta programming language for Datalog.
Answer Set Programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Albeit ASP has been widely adopted in both academic and industrial contexts, it might be difficult for people who are not familiar with logic programming conventions to use it. In this paper, we propose a translation of English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules, and then exemplify the usage of CNL for the specification of well-known combinatorial problems.
There has been increasing interest in enriching ontologies with meta-modeling and meta-querying for the past few years. Unfortunately, the Direct Semantics for OWL2 and SPARQL does not support meta-constructs in a satisfactory way: While meta-axioms can be syntactically expressed using punning, they are not treated as expected semantically. Meta-queries (for example, asking for classes that also occur as individuals) are not defined in SPARQL under the Direct Semantics Entailment Regime. To overcome this, a new semantic flavour for SPARQL, called Metamodeling Semantics Entailment Regime (MSER), has been introduced. In previous work, Cima et al. have proposed a reduction from OWL 2 QL query answering to query answering over Datalog. In this paper, we report on experiments for MSER query answering conducted with various Datalog engines.
This paper is an interim report about an industrial application that uses Datalog combined with empirical methods to compute competitor information from a knowledge graph. The Owler knowledge graph is one of the world’s largest companies information systems (CIS). It contains data about 16+ million companies crowd-sourced from over 1 million experts. In particular, for most companies, it contains a set of competitors. Such competitor information is very useful for many B2B applications such as lead generation . However, competitor relations in crowd-sourced CIS are naturally incomplete. This paper presents CompeGen, a method that applies Vadalog (a particular variant of Datalog) rules to infer new competitor pairs from existing ones in the Owler CIS. Since using the Vadalog inference program alone is insufficient, CompeGen combines its inference process with a “learning” process to acquire some required logical facts and further validates the inference results via an empirical validation process. CompeGen was tested using the companies belonging to the “Internet Software” sector in Owler. It has discovered 23,180 new competitors of which over 80% were correct. We are improving the system and will report further results in the full paper.
Existential rules, also known as tuple-generating dependencies (TGDs) or Datalog+/- rules, are heavily studied in the communities of Knowledge Representation and Reasoning, Semantic Web, and Databases, due to their rich modelling capabilities. In this paper we consider TGDs in the temporal setting, by introducing and studying DatalogMTLE—an extension of metric temporal Datalog (DatalogMTL) obtained by allowing for existential rules in programs. We show that DatalogMTLE is undecidable even in the restricted cases of guarded and weakly-acyclic programs. To address this issue we introduce uniform semantics which, on the one hand, is well-suited for modelling temporal knowledge as it prevents from unintended value invention and, on the other hand, provides decidability of reasoning; in particular, it becomes 2-EXPSPACE-complete for weakly-acyclic programs but remains undecidable for guarded programs. We provide an implementation for the decidable case and demonstrate its practical feasibility. Thus we obtain an expressive, yet decidable, rule-language and a system which is suitable for complex temporal reasoning with existential rules.
