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carlos.raymundo@upc.edu.pe / This paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.

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

There are increasing safety concerns in chemical and petrochemical process industry. The huge explosion of Nowruz oil Field platform that happened in Persian gulf-IRAN at 1983, along with other disastrous events have effected chemical industrial renaissance and led to high demand to enhance safety. Oil and chemical Industries involve complex processes and handle hazardous materials that may potentially cause catastrophic consequences in terms of human losses, injuries, asset lost and environmental stresses. One main reason of such catastrophic events is the lack of effective control and monitoring approaches that are required to achieve successful fault diagnosis and accurate hazard identification. Currently, there are aggressive worldwide efforts to propose an effective, robust, and high accuracy fault propagation analysis and monitoring techniques to prevent undesired events at early stages prior to their occurrence. Among these requirements is the development of an intelligent and automated control and monitoring system to first diagnose faulty equipment and process variable deviations, and then identify hazards associated with faults and deviations. Research into safety and control issues become high priority in all aspects. To support these needs, predictive control and intelligent monitoring system is under study and development at the Energy Safety and Control Laboratory (ESCL) – University of Ontario Institute of Technology (UOIT). The purpose of this research is to present a real time fault propagation analysis method for chemical / petrochemical process industry through fault semantic network (FSN) using accurate process variable interactions (PV-PV interactions). The effectiveness, feasibility, and robustness of the proposed method are demonstrated on simulated data emanating from a well-known Tennessee Eastman (TE) chemical process. Unlike most existing probabilistic approaches, fault propagation analysis module classifies faults and identifies faulty equipment and deviations according to obtained data from the underlying processes. It is an expert system that identifies corresponding causes and consequences and links them together. FSN is an integrated framework that is used to link fault propagation scenarios qualitatively and quantitatively. Probability and fuzzy rules are used for reasoning causes and consequences and tuning FSN. / UOIT

Seguint una concepció clàssica de la Intel•ligència Artificial (aquella que es posava com horitzó una definició dels mecanismes cognitius i la seva implementació en computadors), aquesta tesi s'endinsa en el problema de l'organització del coneixement. En especial, es posa atenció a la memòria semàntica i el coneixement lingüístic, intentant esbrinar de quina forma emergeixen les relacions semàntiques entre paraules. Per assolir aquests objectius es recorre a tres fonts principals: la utilització de dades empíriques provinents de la psicolingüística i la neuropsicologia; l'ús de la metodologia de sistemes complexes (física estadística) per la construcció de models i simulació de dinàmiques; i finalment l'aprofitament de les tecnologies al nostre abast tant per l'obtenció de noves dades (Internet) com una capacitat d'emmagatzemament suficient i velocitat de processament per al tractament de dades massives. D'aquest punt de vista arrelat en la Ciència Cognitiva en poden sorgir aplicacions fortament vinculades a problemes vigents en l'àmbit de Ciències de la Computació, com són l'extracció d'informació no supervisada, l'enriquiment de bases de dades i recursos lingüístics electrònics (Wikipedia, WordNet, etc.) i la millora de sistemes de consulta (query-based systems). Al Capítol 2 s'estableixen les bases metodològiques que han servit per construir la resta del treball. El Capítol 3 es dedica a aclarir (i) quina mena de dades s'han emprat (i s'empren) en l'estudi a gran escala del llenguatge i els fenòmens cognitius que l'envolten; i (ii) es revisen els treballs més destacables que s'han fet fins al moment actual al voltant del llenguatge i la cognició. Al Capítol 4 s'introdueix el Random Inheritance Model, que representa un intent per comprendre com emergeixen la similitud semàntica entre paraules i les categories semàntiques. Els resultats es comparen amb dades empíriques basades en les respostes de subjectes humans. Al Capítol 5 presentem un model de degradació semàntica que emula processos neurodegeneratius i prediu acuradament, a nivell qualitatiu, les observacions experimentals amb malalts d'Alzheimer que s'han fet en l'àmbit de la neuropsicologia. En aquests processos degeneratius convergeixen interessos multidisciplinars, que van de la mateixa cognició al fenomen de percolació en física estadística. El Capítol 6 queda finalment dedicat a una reflexió global d'aquesta memòria. / Following a classical conception of Artificial Intelligence (one that aims a definition of cognitive mechanisms and their implementation in computers), this thesis explores the problem of knowledge organization. In particular, it draws attention to the linguistic and semantic memory, trying to find out how semantic relations emerge between words. To achieve these objectives, we rely on three main sources: use of empirical data from psycholinguistics and neuropsychology; the use of complex systems (statistical physics) methodology to build and simulate dynamic models; and finally the utilization of technologies at our disposal both for obtaining new data (Internet) as well as sufficient storage capacity and processing speed for massive data manipulation. From this point of view, rooted in Cognitive Science, many applications may arise, some of them strongly linked to current problems in the field of Computer Science, such as unsupervised information extraction, enrichment of databases and language electronic resources (Wikipedia, WordNet, etc.). and improve consultation systems (query-based systems). In Chapter 2 the methodologies that have helped build the rest of the work are established. Chapter 3 is devoted to clarify (i) the kind of data that have been used in the large-scale study of language and cognitive phenomena around it, and (ii) review some of the major contributions to the date about language and cognition. In Chapter 4 the Random Inheritance Model is introduced, which represents an attempt to understand how does semantic similarity between words and semantic categories emerge. Results are compared with empirical data obtained from responses with human subjects. In Chapter 5 we present a model of semantic degradation which emulates neurodegenerative processes, and predicts experimental observations from Alzheimer's Disease patients in the field of neuropsychology. In the study of such degenerative processes different multidisciplinary interests converge, ranging from cognition itself to percolation theory in statistical physics. Chapter 6 is finally devoted to a global reflection of this memory.

This thesis details the creation of ASKNet (Automated Semantic Knowledge Network), a system for creating large scale semantic networks from natural language texts. Using ASKNet as an example, we will show that by using existing natural language processing (NLP) tools, combined with a novel use of spreading activation theory, it is possible to efficiently create high quality semantic networks on a scale never before achievable. The ASKNet system takes naturally occurring English text (e.g., newspaper articles), and processes them using existing NLP tools. It then uses the output of those tools to create semantic network fragments representing the meaning of each sentence in the text. Those fragments are then combined by a spreading activation based algorithm that attempts to decide which portions of the networks refer to the same real-world entity. This allows ASKNet to combine the small fragments together into a single cohesive resource, which has more expressive power than the sum of its parts. Systems aiming to build semantic resources have typically either overlooked information integration completely, or else dismissed it as being AI-complete, and thus unachievable. In this thesis we will show that information integration is both an integral component of any semantic resource, and achievable through a combination of NLP technologies and novel applications of spreading activation theory. While extraction and integration of all knowledge within a text may be AI-complete, we will show that by processing large quantities of text efficiently, we can compensate for minor processing errors and missed relations with volume and creation speed. If relations are too difficult to extract, or we are unsure which nodes should integrate at any given stage, we can simply leave them to be picked up later when we have more information or come across a document which explains the concept more clearly. ASKNet is primarily designed as a proof of concept system. However, this thesis will show that it is capable of creating semantic networks larger than any existing similar resource in a matter of days, and furthermore that the networks it creates of are sufficient quality to be used for real world tasks. We will demonstrate that ASKNet can be used to judge semantic relatedness of words, achieving results comparable to the best state-of-the-art systems.

Master of Arts / Department of Communication Studies / William Schenck-Hamlin / The following research project seeks to answer the question: “To what extent can differences among juvenile detention centers be explained on the basis of concepts of restorative and retributive justice?” To investigate, mission statements were collected from a national sample of Juvenile Detention Centers. A semantic network analysis was performed to answer the above research question. The computer program CATPAC was used to create 2-d images of the semantic analysis. From these images eight themes emerged through clusters: institutional identity, public safety, life skill values, family and child tie, and community and family tie, support from staff, support from environment, and support from environment and staff. These themes were reflective of retributive or restorative orientation. Results indicate that male public institutions are reflective of retributive justice while female public, male private, and female private institutions are more reflective of restorative justice. These findings suggest biases and treatment patterns within the juvenile justice system.

Our purpose was to explore developmental changes in the organization and access to the mental lexicon between the ages of three-, five-, and seven years. Six-hundred and seventy three Mandarin-speaking participants listed all exemplars of animals and foods that came to mind within two one-minute intervals. Compared to younger participants, the older children demonstrated more correct responses and fewer errors, suggesting that they have greater knowledge of category-relevant vocabulary. They produced more subcategories, many of which involved embedding and overlapping, which suggests they have more sophisticated lexical-semantic organization. Also, they produced fewer and less closely spaced repetitions, suggesting they could more effectively monitor retrieval responses. We conclude that between the ages of three to seven, children expand and refine the organization of their mental lexicons. Improved monitoring may reflect growth in executive functioning.

The Unified Medical Language System Semantic Network (UMLS SN) being an upper-level abstraction of the biomedical domain has a complex structure due to many relationships, making it difficult for human orientation. Therefore, while the SN is a valuable source for modeling contents of the biomedical domain its usage is limited. NetON was designed and built for the automatic transformation of UMLS SN to OWL sublanguages to support semantic operations between biomedical systems. NetON uses advances in the Semantic Web, a candidate technology for sustaining knowledge intensive tasks. Ontology Web Language (OWL) sublanguage rules are used to represent information in UMLS SN. The major contribution of NetON is the opportunity of automatic transformation of UMLS SN to OWL sublanguages named as OWL Basic Species. The aim of NetON is maximum possible information transformation from UMLS SN. The only information that is not able to be transformed to any OWL Basic Species due to the lack of appropriate constructors in OWL standard is inheritance blockings in UMLS SN. In UMLS SN, there are unseen assertions that can be inferred by using inference rules on explicitly specified assertions which are not essentially valid for all the descendants. Deduction outcomes of any OWL reasoners on NetON OWL Basic Species will also include false positives due to the lack of inheritance blocking information. The algorithms of the second dimension consider the inheritance blocking information while executing inference rules. As this cannot be done by any OWL reasoner, the second dimension offers a solution for application developers.

Šiame darbe analizuojamos pasaulinio semantinio tinklo technologijos dalykinės srities žinioms užrašyti ir valdyti. Darbo metu buvo papildyta dalykinės srities ontologija bei sukurta nauja sistėminė vartotojo sąsajos formavimo ontologija, kuri palaiko daugiakalbystę. Realizuotos ir aprobuotos užklausos ir automatizavimo procesai dirbantys su minėtomis ontologijomis. Darbo vykdymo metu sukauptos žinios buvo surinktos, susistemintos ir pateikiamos kaip darbo metodika. Remiantis šia metodika sukurta eksperimentinė sistema, skirta padėti identifikuoti su BIOS klaidomis susijusias problemas ir/arba operacinių sistemų (OS) sutrikimo priežastis ir jas spręsti. Apie sukurtą sistemą buvo perskaitytas pranešimas konferencijoje „Mokslas ir studijos 2010: teorija ir praktika“, kuri įvyko Šiaurės Lietuvos kolegijoje. Metodiką galima pritaikyti bet kokiai dalykinei sričiai, tiek realizuojant e-mokymo(si) sistemose probleminio mokymosi principus, tiek paramos paslaugų teikimo prekės ar produkto vartotojui sferoje. / This work examines the global semantic web technologies in the subject area knowledge and record management. Work was completed in the subject area and Ontology, a new user interface making systemic ontology, which supports multilingualism. Realized and dealer inquiries, and automate processes in working with these ontologies. Work during the accumulated knowledge has been collected, systematised and presented as a working methodology. Based on the methodology developed an experimental system designed to help identify errors in the BIOS-related problems and / or operating systems (OS) and cause disruption to solve them. This system of notification was read in the conference of "Education and training 2010: Theory and Practice", which took place in Northern Lithuania College. The approach can be applied to any subject area, and implement e-learning (learning) problem of learning the principles of systems and support services or goods to the consumer product area.