933 research outputs found
The Bag Semantics of Ontology-Based Data Access
Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags
Using Ontologies to Query Probabilistic Numerical Data: Extended Version
We consider ontology-based query answering in a setting where some of the data are numerical and of a probabilistic nature, such as data obtained from uncertain sensor readings. The uncertainty for such numerical values can be more precisely represented by continuous probability distributions than by discrete probabilities for numerical facts concerning exact values. For this reason, we extend existing approaches using discrete probability distributions over facts by continuous probability distributions over numerical values. We determine the exact (data and combined) complexity of query answering in extensions of the well-known description logics EL and ALC with numerical comparison operators in this probabilistic setting.This is an extended version of the article in: Proceedings of the 11th International Symposium on Frontiers of Combining Systems. This version has been revised based on the comments of the reviewers
Query Answering in Probabilistic Data and Knowledge Bases
Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory
Answering SPARQL queries modulo RDF Schema with paths
SPARQL is the standard query language for RDF graphs. In its strict
instantiation, it only offers querying according to the RDF semantics and would
thus ignore the semantics of data expressed with respect to (RDF) schemas or
(OWL) ontologies. Several extensions to SPARQL have been proposed to query RDF
data modulo RDFS, i.e., interpreting the query with RDFS semantics and/or
considering external ontologies. We introduce a general framework which allows
for expressing query answering modulo a particular semantics in an homogeneous
way. In this paper, we discuss extensions of SPARQL that use regular
expressions to navigate RDF graphs and may be used to answer queries
considering RDFS semantics. We also consider their embedding as extensions of
SPARQL. These SPARQL extensions are interpreted within the proposed framework
and their drawbacks are presented. In particular, we show that the PSPARQL
query language, a strict extension of SPARQL offering transitive closure,
allows for answering SPARQL queries modulo RDFS graphs with the same complexity
as SPARQL through a simple transformation of the queries. We also consider
languages which, in addition to paths, provide constraints. In particular, we
present and compare nSPARQL and our proposal CPSPARQL. We show that CPSPARQL is
expressive enough to answer full SPARQL queries modulo RDFS. Finally, we
compare the expressiveness and complexity of both nSPARQL and the corresponding
fragment of CPSPARQL, that we call cpSPARQL. We show that both languages have
the same complexity through cpSPARQL, being a proper extension of SPARQL graph
patterns, is more expressive than nSPARQL.Comment: RR-8394; alkhateeb2003
Reasoning in Many Dimensions : Uncertainty and Products of Modal Logics
Probabilistic Description Logics (ProbDLs) are an extension of Description Logics that are designed to capture uncertainty. We study problems related to these logics. First, we investigate the monodic fragment of Probabilistic first-order logic, show that it has many nice properties, and are able to explain the complexity results obtained for ProbDLs. Second, in order to identify well-behaved, in best-case tractable ProbDLs, we study the complexity landscape for different fragments of ProbEL; amongst others, we are able to identify a tractable fragment. We then study the reasoning problem of ontological query answering, but apply it to probabilistic data. Therefore, we define the framework of ontology-based access to probabilistic data and study the computational complexity therein. In the final part of the thesis, we study the complexity of the satisfiability problem in the two-dimensional modal logic KxK. We are able to close a gap that has been open for more than ten years
Query Rewriting and Optimization for Ontological Databases
Ontological queries are evaluated against a knowledge base consisting of an
extensional database and an ontology (i.e., a set of logical assertions and
constraints which derive new intensional knowledge from the extensional
database), rather than directly on the extensional database. The evaluation and
optimization of such queries is an intriguing new problem for database
research. In this paper, we discuss two important aspects of this problem:
query rewriting and query optimization. Query rewriting consists of the
compilation of an ontological query into an equivalent first-order query
against the underlying extensional database. We present a novel query rewriting
algorithm for rather general types of ontological constraints which is
well-suited for practical implementations. In particular, we show how a
conjunctive query against a knowledge base, expressed using linear and sticky
existential rules, that is, members of the recently introduced Datalog+/-
family of ontology languages, can be compiled into a union of conjunctive
queries (UCQ) against the underlying database. Ontological query optimization,
in this context, attempts to improve this rewriting process so to produce
possibly small and cost-effective UCQ rewritings for an input query.Comment: arXiv admin note: text overlap with arXiv:1312.5914 by other author
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