Spelling suggestions: "subject:"incremental reasoning"" "subject:"ncremental reasoning""
1 |
Raisonnement incrémental sur des flux de données / Incremental reasoning over triple streamsChevalier, Jules 05 February 2016 (has links)
Nous proposons dans cette thèse une architecture pour le raisonnement incrémental sur des flux de triples. Afin de passer à l’échelle, elle est conçue sous la forme de modules indépendants, permettant l’exécution parallèle du raisonnement. Plusieurs instances d’une même règle peuvent être exécutées simultanément afin d’améliorer les performances. Nous avons également concentré nos efforts pour limiter la dispersion des doublons dans le système, problème récurrent du raisonnement. Pour cela, un triplestore partagé permet à chaque module de filtrer au plus tôt les doublons. La structure de notre architecture, organisée en modules indépendants par lesquels transitent les triples, lui permet de recevoir en entrée des flux de triples. Enfin, notre architecture est indépendante du fragment utilisé. Nous présentons trois modes d’inférence pour notre architecture. Le premier consiste à inférer l’ensemble des connaissances implicites le plus rapidement possible. Le second priorise l'inférence de certaines connaissances prédéterminées. Le troisième vise à maximiser la quantité de triples inférés par seconde. Nous avons implémenté l’architecture présentée à travers Slider, un raisonneur incrémental prenant nativement en charge les fragments ρdf et RDFS. Il peut être facilement étendu à des fragments plus complexes. Nos expérimentations ont montré une amélioration des performances de plus de 65% par rapport au raisonneur OWLIM-SE. Nous avons également mené des tests montrant que l’utilisation du raisonnement incrémental avec Slider apporte un avantage systématique aux performances par rapport au raisonnement par lots, quels que soient l’ontologie utilisée et le fragment appliqué / In this thesis, we propose an architecture for incremental reasoning on triple streams. To ensure scalability, it is composed of independent modules; thus allowing parallel reasoning. That is, several instances of a same rule can be simultaneously executed to enhance performance. We also focused our efforts to limit the duplicates spreading in the system, a recurrent issue for reasoning. To achieve this, we design a shared triplestore which allows each module to filter duplicates as soon as possible. The triples passes through the different independent modules of the architecture allows the reasoner to receive triple streams as input. Finally, our architecture is of agnostic nature regarding the fragment used for the inference. We also present three inference modes for our architecture: the first one infers all the implicit knowledge as fast as possible; the second mode should be used when the priority has to be defined for the inference of a specific type of knowledge; the third one proposes to maximize the amount of triples inferred per second. We implemented this architecture through Slider, an incremental reasoning natively supporting the fragments ρdf and RDFS: It can easily be extended to more complex fragments. Our experimentations show a 65% improvement over the reasoner OWLIM-SE. However, the recently published reasoner RDFox exhibits better performance, although this one does not provide prioritized inference. We also conducted experimentations showing that the use of incremental reasoning over batch-based reasoning offers systematically better performance for all the ontologies and fragments used
|
2 |
A Bayesian learning approach to inconsistency identification in model-based systems engineeringHerzig, Sebastian J. I. 08 June 2015 (has links)
Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.
|
Page generated in 0.0756 seconds