<p>Doktorska disertacija se bavi istraživanjima koja imaju za cilj da unaprede<br />obrazovanje nastavničkog kadra iz oblasti informatike. Predmet istraživanja disertacije je<br />razvoj platforme koja će olak&scaron;ati sinhronizaciju razvoja nastavničkog kurikuluma iz<br />informatičke oblasti sa aktuelnim srednjo&scaron;kolskim informatičkim standardima i<br />savremenim kurikulumima za nastavnike informatike. Prvi pravac istraživanja je<br />identifikovanje stanja u oblasti obrazovanja nastavnika informatike (modaliteti<br />obrazovanja nastavnika i kompetencije koje se tim modalitetima stiču). U disertaciji se<br />stanje identifikuje na osnovu analize savremene literature, aktuelnih standarda,<br />reprezentativnih kurikuluma i preliminarnog istraživanja nad populacijom sada&scaron;njih<br />nastavnika informatike na teritoriji Vojvodine. Drugi pravac istraživanja je<br />obezbeđivanje (tehničke) pretpostavke za objektivnije i efikasnije kreiranje,<br />upoređivanje i razmenu kurikuluma. Taj pravac se odnosi na razvoj ma&scaron;inski čitljive<br />ontologije kurikuluma bazirane na većpostojećim standardima u domenu obrazovanja i<br />softverske podr&scaron;ke za upoređivanje takvih reprezentacija kurikuluma bazirane na<br />tehnologijama Semantičkog Weba.<br />Ciljevi istraživanja u disertaciji su specifikacija ma&scaron;inski čitljivog modela informatičkog<br />kurikuluma za obrazovanje nastavnika informatike, ma&scaron;inski&nbsp; čitljivog modela<br />informatičkog kurikuluma za srednjo&scaron;kolski nivo obrazovanja i razvoj prototipa<br />softverske aplikacije za verifikaciju predloženih modela.</p> / <p>The doctoral thesis deals with researches that aim to improve the education of&nbsp;teaching staff in the field of informatics. The subject of the dissertation research is to&nbsp;develop a platform that will facilitate the synchronization of the development of teaching&nbsp; curricula in the informatics field with the current secondary informatics standards and modern curricula for informatics teachers of informatics. The first research direction is to identify the state in the informatics teacher education (modalities of teacher education and competences acquired through these modalities). In the dissertation, the state is identified from the review of literature, currentstandards, representative curricula and a preliminary research on population of current informatics teachers in Vojvodina. Another research direction is to provide (technical) prerequisites for more objective and more efficient creation, comparison and exchange of curricula. That direction is related to the development of a machine-readable ontological representation of a curriculum based on existing standards in the field of education and to developing software support for comparing such representations of curricula based on the Semantic Web technologies.<br />The goals of the research in the dissertation are to specify a machine readable model of the informatics teacher education curriculum, a machine-readable model of the informatics curriculum for secondary level education and the development of a software application prototype in order to verify the proposed models.</p>

The session-based SAMBO is an ontology alignment system involving MySQL to store matching results. Currently, SAMBO is able to align most ontologies within acceptable time. However, when it comes to large scale ontologies, SAMBO fails to reach the target. Thus, the main purpose of this thesis work is to improve the performance of SAMBO, especially in the case of matching large scale ontologies.  To reach the purpose, a comprehensive literature study and an investigation on two outstanding large scale ontology system are carried out with the aim of setting the improvement directions. A detailed investigation on the existing SAMBO is conducted to figure out in which aspects the system can be improved. Parallel matching process optimization and data management optimization are determined as the primary optimization goal of the thesis work. In the following, a few relevant techniques are studied and compared. Finally, an optimized design is proposed and implemented.  System testing results of the improved SAMBO show that both parallel matching process optimization and data management optimization contribute greatly to improve the performance of SAMBO. However the execution time of SAMBO to align large scale ontologies with database interaction is still unacceptable.

Using ontologies is a key technology in the semantic web. The semantic web helps people to store their data on the web, build vocabularies, and has written rules for handling these data and also helps the search engines to distinguish between the information they want to access in web easier. In order to use multiple ontologies created by different experts we need matchers to find the similar concepts in them to use it to merge these ontologies. Text based searches use the string similarity functions to find the equivalent concepts inside ontologies using their names.This is the method that is used in lexical matchers. But a global standard for naming the concepts in different research area does not exist or has not been used. The same name may refer to different concepts while different names may describe the same concept. To solve this problem we can use another approach for calculating the similarity value between concepts which is used in structural and constraint-based matchers. It uses relations between concepts, synonyms and other information that are stored in the ontologies. Another category for matchers is instance-based that uses additional information like documents related to the concepts of ontologies, the corpus, to calculate the similarity value for the concepts. Decision trees in the area of data mining are used for different kind of classification for different purposes. Using decision trees in an instance-based matcher is the main concept of this thesis. The results of this implemented matcher using the C4.5 algorithm are discussed. The matcher is also compared to other matchers. It also is used for combination with other matchers to get a better result.

SAMBO is a system to assist users for alignment and merging of two ontologies (i.e. to find inter-ontology relationship). The user performs an alignment process with the help of mapping suggestions. The objective of the thesis work is to extend the existing system with new components; multiple sessions, integration of an ontology alignment strategy, recommendation system, integration of a system that can use results from previous sessions, and integration of partial reference alignment (PRA) that can be used to filter mapping suggestions. Most of the theoretical work existed, but it was important to study and implement, how these components can be integrated in the system, and how they can work together.

Business-IT alignment has been a top research topic for three decades now and consistently ranks high on CIO priorities and concerns. In spite of its seeming advantages, sustainable business-IT alignment remains elusive in practice. This can be attributed to the language and knowledge gaps which impede mutual understanding between business and IT stakeholders. It can also be attributed to the limitations imposed by approaching alignment solely from a strategic perspective. This thesis argues for an ontology-based framework that bridges the language and knowledge gaps through closer interaction between business and IT stakeholders throughout the software development and project management lifecycles, especially at the requirements engineering stage. Attempts at achieving sustainable business-IT alignment predominantly focus on strategic alignment and have not been successful for various reasons. Firstly, driving down alignment initiatives to the operational and tactical levels is challenging. Secondly, it is difficult to operationalize the metrics used for evaluating alignment maturity at strategic levels. These limitations are less pronounced at the functional levels of an organization. It is at these levels that business strategies are executed and interaction between business and IT personnel is most frequent. The interaction between business and IT stakeholders in the execution of IT projects presents an opportunity that can be leveraged to drive alignment maturity. The proposed framework is discussed in terms of its underpinning hypotheses, workflows, tool design and implementation, its use with a third party framework and tool. Antecedents to operational and tactical alignment such as quality, reuse, communication, learning, and shared understanding, are proposed as a practical means of achieving sustainable alignment maturity. The framework is applied to real world, business-critical projects in a top global financial services organization and validated using descriptive statistical analysis and structural equation modelling techniques. Contributions made through the study are highlighted. This includes the Alignment Forces Model which unifies the proposed framework and its support tool within software development and project management lifecycles. The Alignment Forces model and how it can be applied in practice is presented. Results of the quantitative data analyses indicate support for the arguments for the framework towards improving business-IT alignment, however with some limitations. Results also indicate support for the hypotheses for the antecedents to sustainable alignment maturity at lower organizational levels put forward. Finally, suggestions on furthering the study, addressing its limitations, and refining the framework and tool are articulated.

Na área de emparelhamento de ontologias, são utilizadas algumas métricas para avaliar os alinhamentos produzidos. As métricas baseadas em alinhamento têm como princípio básico confrontar um alinhamento proposto com um alinhamento de referência. Algumas destas métricas, entretanto, não têm alcançado êxito suficiente porque (i) não conseguem discriminar sempre entre um alinhamento totalmente errado e um quase correto; e (ii) não conseguem estimar o esforço do usuário para refinar o alinhamento resultante. Este trabalho tem como objetivo apresentar uma nova abordagem para avaliar os alinhamentos de ontologias. A nossa abordagem apresenta uma métrica na qual utilizamos as próprias consultas normalmente já realizadas nas ontologias originais para julgar a qualidade do alinhamento proposto. Apresentamos também alguns resultados satisfatórios de nossa abordagem em relação às outras métricas já existentes e largamente utilizadas. / In the ontology matching field, different metrics are used to evaluate the resulting alignments. Metrics based on alignment adopt the basic principle of verifying a proposed alignment against a reference alignment. Some of these metrics do not achieve good results because (i) they cannot always distinguish between a totally wrong alignment and one which is almost correct; and (ii) they cannot estimate the effort for the user to refine the resulting alignment. This work aims to present a new approach to evaluate ontology alignments. Our approach presents a measure that uses the usual queries in the original ontologies to assess the quality of the proposed alignment. We also present some satisfactory results of our approach with regard to widely used metrics.

With the popularity of the World Wide Web, a large amount of data is generated and made available through the Internet everyday. To integrate and query this huge amount of heterogeneous data, the vision of Semantic Web has been recognized as a possible solution. One key technology for the Semantic Web is ontologies. Many ontologies have been developed in recent years. Meanwhile, due to the demand of applications using multiple ontologies,  mappings between entities of these ontologies are generated as well, which leads to the generation of ontology networks consisting of ontologies and mappings between these ontologies. However, neither developing ontologies nor finding mappings between ontologies is an easy task. It may happen that the ontologies are not consistent or complete, or the mappings between these ontologies are not correct or complete, or the resulting ontology network is not consistent. This may lead to problems when they are used in semantically-enabled applications. In this thesis, we address two issues relevant to the quality of the mappings and the structure in the ontology network. The first issue deals with the missing mappings between networked ontologies. Assuming existing mappings between ontologies are correct, we investigate whether and how to use these existing mappings, to find more mappings between ontologies. We propose and test several strategies of using the given correct mappings to align ontologies. The second issue deals with the missing structure, in particular missing is-a relations, in networked ontologies. Based on the assumption that missing is-a relations are a kind of modeling defects, we propose an ontology debugging approach to tackle this issue. We develop an algorithm for detecting missing is-a relations in ontologies, as well as algorithms which assist the user in repairing by generating and recommending possible ways of repairing and executing the repairing. Based on this approach, we develop a system and test its use and performance.

Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. The combination of ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This process requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain. This thesis presents use cases where we applied ontology composition, integration, and alignment of phenotype ontologies, and evaluated the resulting ontologies and alignment. First, we analyzed a large aging dataset of inbred laboratory mice, using Mouse Anatomy and Mouse Pathology ontologies. Second, we integrated phenotype ontologies for human and model organism phenotypes to enable comparisons of phenotypes between and within individual species. We developed Pheno-e, an extension of PhenomeNet. We identified novel abnormal anatomical classes for fly phenotypes, allowing the annotation of fly genes that were not annotated before. We demonstrate the distinct contributions of each species' phenotypic data to detecting human diseases using Pheno-e, and show that mouse phenotypic data contributes the most to the discovery of gene--disease associations. This work could guide the selection of model organisms when building methods to find gene-disease associations. Additionally, we refined class definitions in phenotypic ontologies, specifically targeting cell cardinality phenotypes. This representation resolved incorrect inferences in the utilized ontologies, enabling accurate interpretation of phenotypic descriptions. Our findings reveal that this correction enhances gene-disease prediction for diseases associated with cardinality phenotypes. Third, we introduce a novel neural-symbolic method that combines logic fundamentals with machine learning for ontology alignment. This method begins with symbolic representation, followed by iterative neural learning for alignment and symbolic representation consistency checking and reasoning, and back to neural learning. We demonstrate that our system generates noncontroversial alignments first and these alignments are coherent with respect to OWL EL. This novel method can pave the way for more accurate and efficient ontology-based methods, which can have significant implications for various semantic web applications.

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