Minimal Deductive Systems for RDF

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process.

Annals of Mathematics and Artificial Intelligence, 2015

ERDF stable model semantics is a recently proposed semantics for ERDF ontologies and a faithful extension of RDFS semantics on RDF graphs. In this paper, we elaborate on the computability and complexity issues of the ERDF stable model semantics. Based on the undecidability result of ERDF stable model semantics, decidability under this semantics cannot be achieved, unless ERDF ontologies of restricted syntax are considered. Therefore, we propose a slightly modified semantics for ERDF ontologies, called ERDF #nstable model semantics. We show that entailment under this semantics is, in general, decidable and also extends RDFS entailment. Equivalence statements between the two semantics are provided. Additionally, we provide algorithms that compute the ERDF #n-stable models of syntax-restricted and general ERDF ontologies. Further, we provide complexity results for the ERDF #nstable model semantics on syntax-restricted and general ERDF ontologies. Finally, we provide complexity results for the ERDF stable model semantics on syntax-restricted ERDF ontologies.

International Journal of Web Information Systems, 2014

Purpose – The paper aims to discuss extensions of SPARQL that use regular expressions to navigate RDF graphs and may be used to answer queries considering RDFS semantics (in particular, nSPARQL and our proposal CPSPARQL). Design/methodology/approach – The paper is based upon a theoretical comparison of the expressiveness and complexity of both nSPARQL and the corresponding fragment of CPSPARQL, that we call cpSPARQL. Findings – The paper shows that nSPARQL and cpSPARQL (the fragment of CPSPARQL) have the same complexity through cpSPARQL, being a proper extension of SPARQL graph patterns, is more expressive than nSPARQL. Research limitations/implications – It has not been possible to the authors to compare the performance of our CPSPARQL implementation with other proposals. However, the experimentation has allowed to make interesting observations. Practical implications – The paper includes implications for implementing the SPARQL RDFS entailment regime. Originality/value – The paper...

Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems

We present a knowledge graph management system designed to run on Edge computing devices that handles high-frequency data streams. During the design phase, we took into account the inherent limitations of the devices, i.e., limited computing power and storage space, as well as the expectations of applications, e.g., low latency, high throughput, and intelligent data management. This results in a compact, decompression-free, in-memory, streamingenabled RDF store that supports continuous querying and some forms of reasoning. The system addresses efficient query processing of data continuously arriving at a fast pace and is well-adapted to event-driven applications such as anomaly and risk detection. We empirically emphasize its accuracy, robustness, latency, and throughput properties on a real-world IoT setting originating from the energy management domain. CCS CONCEPTS • Information systems → Stream management; • Applied computing → Event-driven architectures; • Computer systems organization → Distributed architectures.

Lecture Notes in Computer Science, 2009

The Semanic Web: Research and Applications, 2011

There is a comprehensive body of theory studying updates and schema evolution of knowledge bases, ontologies, and in particular of RDFS. In this paper we turn these ideas into practice by presenting a feasible and practical procedure for updating RDFS. Along the lines of ontology evolution, we treat schema and instance updates separately, showing that RDFS instance updates are not only feasible, but also deterministic. For RDFS schema update, known to be intractable in the general abstract case, we show that it becomes feasible in real world datasets. We present for both, instance and schema update, simple and feasible algorithms.

The Semantic Web – ISWC 2014, 2014

Updates in RDF stores have recently been standardised in the SPARQL 1.1 Update specification. However, computing entailed answers by ontologies is usually treated orthogonally to updates in triple stores. Even the W3C SPARQL 1.1 Update and SPARQL 1.1 Entailment Regimes specifications explicitly exclude a standard behaviour for entailment regimes other than simple entailment in the context of updates. In this paper, we take a first step to close this gap. We define a fragment of SPARQL basic graph patterns corresponding to (the RDFS fragment of) DL-Lite and the corresponding SPARQL update language, dealing with updates both of ABox and of TBox statements. We discuss possible semantics along with potential strategies for implementing them. In particular, we treat both, (i) materialised RDF stores, which store all entailed triples explicitly, and (ii) reduced RDF Stores, that is, redundancy-free RDF stores that do not store any RDF triples (corresponding to DL-Lite ABox statements) entailed by others already. We have implemented all semantics prototypically on top of an off-the-shelf triple store and present some indications on practical feasibility. 1 We ignore issues like axiomatic triples [13], blank nodes [17], or, in the context of OWL, inconsistencies arising through updates [5]. Neither do we consider named graphs in SPARQL, which is why we talk about "triple stores" as opposed to "graph stores" [8].