We propose a framework for semi-automatically verifying relational database schema mappings for data exchange. Schema mappings for data exchange formally describe how to move data between a source and target database. State-of-the-art schema mapping tools propose several mappings, but require user intervention to determine their semantic correctness. For this, the user must understand the domain the schemas represent and the meanings of individual schema elements in relation to the domain. Our framework eases the task of understanding the domain and schemas and performs preliminary mapping verification. We use a readable, expressive, and formal conceptual model - a domain ontology - to model the source and target schema domain. We model the schema semantics by annotating schema elements with ontology elements. Our mapping verification algorithm rewrites mappings as statements in terms of the ontology, and uses a reasoner to check that the statements are entailed by the ontology.

The Liar Paradox and related semantic antinomies seem to challenge our deepest intuitions about language, truth and logic. Many philosophers believe that to solve them, we must give up either classical logic, or the expressive resources of natural language, or even the “naïve theory of truth” (according to which "P" and “it is true that 'P'” always entail each other). A particularly extreme form of radical surgery is proposed by figures like Graham Priest, who argues for “dialetheism”—the position that some contradictions are actually true—on the basis of the paradoxes. While Priest’s willingness to dispense with the Law of Non-Contradiction may be unpopular in contemporary analytic philosophy, figures as significant as Saul Kripke and Hartry Field have argued that, in light of the paradoxes, we can only save Non-Contradiction at the expense of the Law of the Excluded Middle, abandoning classical logic in favor of a “paracomplete” alternative in which P and ~P can simultaneously fail to hold. I believe that we can do better than that, and I argue for a more conservative approach, which retains not only “monaletheism” (the orthodox position that no sentence, either in natural languages or other language, can have more than one truth-value at a time), but the full inferential resources of classical logic.

Lidée selon laquelle la mémoire déclarative à long terme celle qui nous permet dencoder et de récupérer sur un mode « déclaratif » des connaissances et des souvenirs acquis par le passé renvoie à deux réalités fondamentalement différentes nest pas neuve. Il y a plus de trente ans, en effet, Tulving (1972)proposait de distinguer la mémoire sémantique de la mémoire épisodique. Cette distinction est toujours admise aujourdhui et constitue une référence pour les pratiques dévaluation en neuropsychologie. Non seulement elle fournit une structure pour lanalyse clinique des troubles de la mémoire, mais elle sert également de cadre théorique pour la compréhension du syndrome amnésique (Verfaellie, 2000). Les relations quentretiennent ces deux systèmes mnésiques ainsi que les réseaux neuronaux qui les sous-tendent restent cependant encore lobjet de débats. De plus, les mécanismes impliqués dans lacquisition de nouvelles connaissances sémantiques sont peu connus. Bien que plusieurs études suggèrent que lapprentissage de nouvelles informations sémantiques dépend de mécanismes implicites (par ex., Ashby & Gott, 1988; Ashby & Maddox, 1990, 1992, Ashby, Alfonson-Reese, Turken, & Waldron, 1998; Hinton, 1981) dautres suggèrent que les mécanismes impliqués pourraient être à la fois implicite et explicites (par ex., Gopnik & Meltzoff, 1997) ou encore uniquement explicites (OConnor, Cree, & McRae, in press). En fait, limplication des mécanismes explicites et implicites pourrait dépendre du type de connaissances sémantiques à apprendre : lacquisition de labels pourrait dépendre de mécanismes plus explicites tandis que lapprentissage de catégories ou des caractéristiques de celles-ci pourrait dépendre de mécanismes plus implicites (Pitel et al., 2009). Dans cette perspective, si les mécanismes dapprentissage implicite interviennent dans lacquisition de connaissances sémantiques, il apparaît intéressant de déterminer leur rôle et les relations quentretiennent lacquisition de connaissances sémantiques et le domaine de lapprentissage implicite. Au plan théorique, cette mise en relation permettrait une meilleure compréhension du rôle joué par les processus dapprentissage implicite dans larchitecture cognitive ; par ailleurs, outre les répercussions que lon pourrait en attendre sur le plan des méthodes pédagogiques, une meilleure compréhension du rôle joué par les mécanismes dapprentissage implicite dans lapprentissage de nouvelles connaissances sémantiques devrait conduire au développement de pratiques de revalidation plus adaptées aux capacités de mémoire préservées ou résiduelles présentées par certains patients amnésiques. Dans ce travail, nous commencerons par développer les principaux modèles de la mémoire qui permettront de situer le système sémantique par rapport aux autres systèmes mnésiques. Nous développerons ensuite les modèles plus spécifiques de la mémoire sémantique qui nous aideront à mieux comprendre comment les connaissances sémantiques sont organisées et comment de nouveaux apprentissages peuvent survenir. Enfin, nous mettrons en évidence les éléments qui permettent de penser que les mécanismes dapprentissage implicite sont directement impliqués dans lacquisition de nouvelles connaissances sémantiques.

This is the 15th issue of the working paper series Interdisciplinary Studies on Information Structure (ISIS) of the Sonderforschungsbereich (SFB) 632. This online version contains the Questionnaire on Focus Semantics contributed by Agata Renans, Malte Zimmermann and Markus Greif, members of Project D2 investigating information structural phenomena from a typological perspective. The present issue provides a tool for collecting and analyzing natural data with respect to relevant linguistic questions concerning focus types, focus sensitive particles, and the effects of quantificational adverbs and presupposition on focus semantics. This volume is a supplementation to the Reference manual of the Questionnaire on Information Structure, issued by Project D2 in ISIS 4 (2006).

This is the 15th issue of the working paper series Interdisciplinary Studies on Information Structure (ISIS) of the Sonderforschungsbereich (SFB) 632. This online version contains the Questionnaire on Focus Semantics contributed by Agata Renans, Malte Zimmermann and Markus Greif, members of Project D2 investigating information structural phenomena from a typological perspective. The present issue provides a tool for collecting and analyzing natural data with respect to relevant linguistic questions concerning focus types, focus sensitive particles, and the effects of quantificational adverbs and presupposition on focus semantics. This volume is a supplementation to the Reference manual of the Questionnaire on Information Structure, issued by Project D2 in ISIS 4 (2006).

Eye movements in reading are sensitive to foveal and parafoveal word features. Whereas the influence of orthographic or phonological parafoveal information on gaze control is undisputed, there has been no reliable evidence for early parafoveal extraction of semantic information in alphabetic script. Using a novel combination of the gaze-contingent fast-priming and boundary paradigms, we demonstrate semantic preview benefit when a semantically related parafoveal word was available during the initial 125 ms of a fixation on the pre-target word (Experiments 1 and 2). When the target location was made more salient, significant parafoveal semantic priming occurred only at 80 ms (Experiment 3). Finally, with short primes only (20, 40, 60 ms) effects were not significant but numerically in the expected direction for 40 and 60 ms (Experiment 4). In all experiments, fixation durations on the target word increased with prime durations under all conditions. The evidence for extraction of semantic information from the parafoveal word favors an explanation in terms of parallel word processing in reading.

The interplay between learning and technology is a growing field that is often referred to as Technology Enhanced Learning (TEL). Within this context, learning applications are software components that are useful for learning purposes, such as textbook replacements, information gathering tools, communication and collaboration tools, knowledge modeling tools, rich lab environments that allows experiments etc. When developing learning applications, the choice of technology depends on many factors. For instance, who and how many the intended end-users are, if there are requirements to support in-application collaboration, platform restrictions, the expertise of the developers, requirements to inter-operate with other systems or applications etc. This thesis provides guidance on a how to develop learning applications based on Semantic Web technology. The focus on Semantic Web technology is due to its basic design that allows expression of knowledge at the web scale. It also allows keeping track of who said what, providing subjective expressions in parallel with more authoritative knowledge sources. The intended readers of this thesis include practitioners such as software architects and developers as well as researchers in TEL and other related fields. The empirical part of the this thesis is the experience from the design and development of two learning applications and two supporting frameworks. The first learning application is the web application Confolio/EntryScape which allows users to collect files and online material into personal and shared portfolios. The second learning application is the desktop application Conzilla, which provides a way to create and navigate a landscape of interconnected concepts. Based upon the experience of design and development as well as on more theoretical considerations outlined in this thesis, three major obstacles have been identified: The first obstacle is: lack of non-expert and user friendly solutions for presenting and editing Semantic Web data that is not hard-coded to use a specific vocabulary. The thesis presents five categories of tools that support editing and presentation of RDF. The thesis also discusses a concrete software solution together with a list of the most important features that have crystallized during six major iterations of development. The second obstacle is: lack of solutions that can handle both private and collaborative management of resources together with related Semantic Web data. The thesis presents five requirements for a reusable read/write RDF framework and a concrete software solution that fulfills these requirements. A list of features that have appeared during four major iterations of development is also presented. The third obstacle is: lack of recommendations for how to build learning applications based on Semantic Web technology. The thesis presents seven recommendations in terms of architectures, technologies, frameworks, and type of application to focus on. In addition, as part of the preparatory work to overcome the three obstacles, the thesis also presents a categorization of applications and a derivation of the relations between standards, technologies and application types. / <p>QC 20121105</p>

As an expressive knowledge representation language for Semantic Web, Web Ontology Language (OWL) plays an important role in areas like science and commerce. The problem of maintaining integrity constraints arises because OWL employs the Open World Assumption (OWA) as well as the Non-Unique Name Assumption (NUNA). These assumptions are typically suitable for representing knowledge distributed across the Web, where the complete knowledge about a domain cannot be assumed, but make it challenging to use OWL itself for closed world integrity constraint validation. Integrity constraints (ICs) on ontologies have to be enforced; otherwise conflicting results would be derivable from the same knowledge base (KB). The current trends of incorporating ICs into OWL are based on its query language SPARQL, alternative semantics, or logic programming. These methods usually suffer from limited types of constraints they can handle, and/or inherited computational expensiveness. This dissertation presents a comprehensive and efficient approach to maintaining integrity constraints. The design enforces data consistency throughout the OWL life cycle, including the processes of OWL generation, maintenance, and interactions with other ontologies. For OWL generation, the Paraconsistent model is used to maintain integrity constraints during the relational database to OWL translation process. Then a new rule-based language with set extension is introduced as a platform to allow users to specify constraints, along with a demonstration of 18 commonly used constraints written in this language. In addition, a new constraint maintenance system, called Jena2Drools, is proposed and implemented, to show its effectiveness and efficiency. To further handle inconsistencies among multiple distributed ontologies, this work constructs a framework to break down global constraints into several sub-constraints for efficient parallel validation.

The development of condition monitoring and fault diagnosis systems for wind turbines has received considerable attention in recent years. With wind playing an increasing part in Canada’s electricity demand from renewable resources, installations of new wind turbines are experiencing significant growth in the region. Hence, there is a need for efficient condition monitoring and fault diagnosis systems for wind turbines. Gearbox, as one of the highest risk elements in wind turbines, is responsible for smooth operation of wind turbines. Moreover, the availability of the whole system depends on the serviceability of the gearbox. This work presents signal processing and soft computing techniques to increase the detection and diagnosis capabilities of wind turbine gearbox monitoring systems based on vibration signal analysis. Although various vibration based fault detection and diagnosis techniques for gearboxes exist in the literature, it is still a difficult task especially because of huge background noise and a large solution search space in real world applications. The objective of this work is to develop a novel, intelligent system for reliable and real time monitoring of wind turbine gearboxes. The developed system incorporates three major processes that include detecting the faults, extracting the features, and making the decisions. The fault detection process uses intelligent filtering techniques to extract faulty information buried in huge background noise. The feature extraction process extracts fault-sensitive and vibration based transient features that best describe the health of the gearboxes. The decision making module implements probabilistic decision theory based on Bayesian inference. This module also devises an intelligent decision theory based on fuzzy logic and fault semantic network. Experimental data from a gearbox test rig and real world data from wind turbines are used to verify the viability, reliability, and robustness of the methods developed in this thesis. The experimental test rig operates at various speeds and allows the implementation of different faults in gearboxes such as gear tooth crack, tooth breakage, bearing faults, iv and shaft misalignment. The application of hybrid conventional and evolutionary optimization techniques to enhance the performance of the existing filtering and fault detection methods in this domain is demonstrated. Efforts have been made to decrease the processing time in the fault detection process and to make it suitable for the real world applications. As compared to classic evolutionary optimization framework, considerable improvement in speed has been achieved with no degradation in the quality of results. The novel features extraction methods developed in this thesis recognize the different faulty signatures in the vibration signals and estimate their severity under different operating conditions. Finally, this work also demonstrates the application of intelligent decision support methods for fault diagnosis in gearboxes. / UOIT

Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.