79 research outputs found
Mapping Data to Ontologies with Exceptions Using Answer Set Programming
In ontology-based data access (OBDA), databases are connected to an ontology via mappings from queries over the database to queries over the ontology. In this paper, we define an ASP-based semantics for mappings from relational databases to first-order ontologies, augmented with queries over the ontology in the mapping rule bodies. The resulting formalism can be described as ”ASP modulo theories”, and can be used to express constraints and exceptions in OBDA systems, as well as being a powerful mechanism for succinctly representing OBDA mappings. Furthermore, we show that brave reasoning in this setting has either the same data complexity as ASP, or is at least as hard as the complexity of checking entailment for the ontology queries. Moreover, despite the interaction of ASP rules and the ontology, most properties of ASP are preserved. Finally, we show that for ontologies with UCQ-rewritable queries there exists a natural reduction from our framework to ASP with existential variables
Repairs of databases with null values
Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaDatabases store information that is intended to model the real world and to help in
modeling, they use constraints that shape the information according to the world view.
However, when a new constraint is defined, the data contained in the database may not
respect it and so the database should be repaired. Those repairs are made by adding,
removing or updating tuples, making as few modifications as possible to satisfy the constraints.
In order to determine the repairs of a database with respect to new constraints,
there are already some available approaches that provide a solution. But databases also
need to contain information that is absence, which is represented through null values.
Null values are not regular values and they represent information that is missing or
unknown. When using null values, there is no consensus in the literature on how to
interpret them when checking constraint satisfaction. Also, there is not a practical implementation to do the repairing regarding null values.
In this document, we study the problem of dealing with null values in the repairing
process and propose a (both practical and theoretically sound) solution for this problem including the definition of semantics for null values to achieve constraint satisfaction, and how to proceed to make the databases repairs, ending with a practical implementation of the proposed solution using Answer-set Programming.FCT project ASPEN - Answer Set Programming
with BoolEaN Satisfiability (PTDC/EIA-CCO/110921/2009
Magic Sets for Disjunctive Datalog Programs
In this paper, a new technique for the optimization of (partially) bound
queries over disjunctive Datalog programs with stratified negation is
presented. The technique exploits the propagation of query bindings and extends
the Magic Set (MS) optimization technique.
An important feature of disjunctive Datalog is nonmonotonicity, which calls
for nondeterministic implementations, such as backtracking search. A
distinguishing characteristic of the new method is that the optimization can be
exploited also during the nondeterministic phase. In particular, after some
assumptions have been made during the computation, parts of the program may
become irrelevant to a query under these assumptions. This allows for dynamic
pruning of the search space. In contrast, the effect of the previously defined
MS methods for disjunctive Datalog is limited to the deterministic portion of
the process. In this way, the potential performance gain by using the proposed
method can be exponential, as could be observed empirically.
The correctness of MS is established thanks to a strong relationship between
MS and unfounded sets that has not been studied in the literature before. This
knowledge allows for extending the method also to programs with stratified
negation in a natural way.
The proposed method has been implemented in DLV and various experiments have
been conducted. Experimental results on synthetic data confirm the utility of
MS for disjunctive Datalog, and they highlight the computational gain that may
be obtained by the new method w.r.t. the previously proposed MS methods for
disjunctive Datalog programs. Further experiments on real-world data show the
benefits of MS within an application scenario that has received considerable
attention in recent years, the problem of answering user queries over possibly
inconsistent databases originating from integration of autonomous sources of
information.Comment: 67 pages, 19 figures, preprint submitted to Artificial Intelligenc
Kiel Declarative Programming Days 2013
This report contains the papers presented at the Kiel Declarative Programming Days 2013, held in Kiel (Germany) during September 11-13, 2013. The Kiel Declarative Programming Days 2013 unified the following events: * 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013) * 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013) * 27th Workshop on Logic Programming (WLP 2013) All these events are centered around declarative programming, an advanced paradigm for the modeling and solving of complex problems. These specification and implementation methods attracted increasing attention over the last decades, e.g., in the domains of databases and natural language processing, for modeling and processing combinatorial problems, and for high-level programming of complex, in particular, knowledge-based systems
Proceedings of the 11th Workshop on Nonmonotonic Reasoning
These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general
Proceedings of the International Workshop on Reactive Concepts in Knowledge Representation 2014
These are the proceedings of the International Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014), which took place on August 19th, 2014 in Prague, co-located with the 21st European Conference on Artificial Intelligence (ECAI 2014)
Fundamental Approaches to Software Engineering
This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution
Arithmetic and Modularity in Declarative Languages for Knowledge Representation
The past decade has witnessed the development of many important declarative languages for knowledge representation and reasoning such as answer set programming (ASP) languages and languages that extend first-order logic. Also, since these languages depend on background solvers, the recent advancements in the efficiency of solvers has positively affected the usability of such languages. This thesis studies extensions of knowledge representation (KR) languages with arithmetical operators and methods to combine different KR languages. With respect to arithmetic in declarative KR languages, we show that existing KR languages suffer from a huge disparity between their expressiveness and their computational power. Therefore, we develop an ideal KR language that captures the complexity class NP for arithmetical search problems and guarantees universality and efficiency for solving such problems. Moreover, we introduce a framework to language-independently combine modules from different KR languages. We study complexity and expressiveness of our framework and develop algorithms to solve modular systems. We define two semantics for modular systems based on (1) a model-theoretical view and (2) an operational view on modular systems. We prove that our two semantics coincide and also develop mechanisms to approximate answers to modular systems using the operational view. We augment our algorithm these approximation mechanisms to speed up the process of solving modular system. We further generalize our modular framework with supported model semantics that disallows self-justifying models. We show that supported model semantics generalizes our two previous model-theoretical and operational semantics. We compare and contrast the expressiveness of our framework under supported model semantics with another framework for interlinking knowledge bases, i.e., multi-context systems, and prove that supported model semantics generalizes and unifies different semantics of multi-context systems. Motivated by the wide expressiveness of supported models, we also define a new supported equilibrium semantics for multi-context systems and show that supported equilibrium semantics generalizes previous semantics for multi-context systems. Furthermore, we also define supported semantics for propositional programs and show that supported model semnatics generalizes the acclaimed stable model semantics and extends the two celebrated properties of rationality and minimality of intended models beyond the scope of logic programs
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