A framework for integrating and transforming between ontologies and relational databases

Bridging the gap between ontologies, expressed in the Web Ontology Language (OWL), and relational databases is a necessity for realising the Semantic Web vision. Relational databases are considered a good solution for storing and processing ontologies with a large amount of data. Moreover, the vast majority of current websites store data in relational databases, and therefore being able to generate ontologies from such databases is important to support the development of the Semantic Web. Most of the work concerning this topic has either (1) extracted an OWL ontology from an existing relational database that represents as exactly as possible the relational schema, using a limited range of OWL modelling constructs, or (2) extracted a relational database from an existing OWL ontology, that represents as much as possible the OWL ontology. By way of contrast, this thesis proposes a general framework for transforming and mapping between ontologies and databases, via an intermediate low-level Hyper-graph Data Model. The transformation between relational and OWL schemas is expressed using directional Both-As-View mappings, allowing a precise definition of the equivalence between the two schemas, hence data can be mapped back and forth between them. In particular, for a given OWL ontology, we interpret the expressive axioms either as triggers, conforming to the Open-World Assumption, that performs a forward-chaining materialisation of inferred data, or as constraints, conforming to the Closed-World Assumption, that performs a consistency checking. With regards to extracting ontologies from relational databases, we transform a relational database into an exact OWL ontology, then enhance it with rich OWL 2 axioms, using a combination of schema and data analysis. We then apply machine learning algorithms to rank the suggested axioms based on past users’ relevance. A proof-of-concept tool, OWLRel, has been implemented, and a number of well-known ontologies and databases have been used to evaluate the approach and the OWLRel tool.Open Acces

Knowledge management and Discovery for advanced Enterprise Knowledge Engineering

2012 - 2013The research work addresses mainly issues related to the adoption of models, methodologies and knowledge management tools that implement a pervasive use of the latest technologies in the area of Semantic Web for the improvement of business processes and Enterprise 2.0 applications. The first phase of the research has focused on the study and analysis of the state of the art and the problems of Knowledge Discovery database, paying more attention to the data mining systems. The most innovative approaches which were investigated for the "Enterprise Knowledge Engineering" are listed below. In detail, the problems analyzed are those relating to architectural aspects and the integration of Legacy Systems (or not). The contribution of research that is intended to give, consists in the identification and definition of a uniform and general model, a "Knowledge Enterprise Model", the original model with respect to the canonical approaches of enterprise architecture (for example with respect to the Object Management - OMG - standard). The introduction of the tools and principles of Enterprise 2.0 in the company have been investigated and, simultaneously, Semantic Enterprise based appropriate solutions have been defined to the problem of fragmentation of information and improvement of the process of knowledge discovery and functional knowledge sharing. All studies and analysis are finalized and validated by defining a methodology and related software tools to support, for the improvement of processes related to the life cycles of best practices across the enterprise. Collaborative tools, knowledge modeling, algorithms, knowledge discovery and extraction are applied synergistically to support these processes. [edited by author]XII n.s

Chord Sequence patterns in OWL

This thesis addresses the representation of and reasoning on musical knowledge in the Semantic Web. The Semantic Web is an evolving extension of the World Wide Web that aims at describing information that is distributed on the web in a machine-processable form. Existing approaches to modelling musical knowledge in the context of the Semantic Web have focused on metadata. The description of musical content and reasoning as well as integration of content descriptions and metadata are yet open challenges. This thesis discusses the possibilities of representing musical knowledge in the Web Ontology Language (OWL) focusing on chord sequence representation and presents and evaluates a newly developed solution. The solution consists of two main components. Ontological modelling patterns for musical entities such as notes and chords are introduced in the (MEO) ontology. A sequence pattern language and ontology (SEQ) has been developed that can express patterns in a form resembling regular expressions. As MEO and SEQ patterns both rewrite to OWL they can be combined freely. Reasoning tasks such as instance classification, retrieval and pattern subsumption are then executable by standard Semantic Web reasoners. The expressiveness of SEQ has been studied, in particular in relation to grammars. The complexity of reasoning on SEQ patterns has been studied theoretically and empirically, and optimisation methods have been developed. There is still great potential for improvement if specific reasoning algorithms were developed to exploit the sequential structure, but the development of such algorithms is outside the scope of this thesis. MEO and SEQ have also been evaluated in several musicological scenarios. It is shown how patterns that are characteristic of musical styles can be expressed and chord sequence data can be classified, demonstrating the use of the language in web retrieval and as integration layer for different chord patterns and corpora. Furthermore, possibilities of using SEQ patterns for harmonic analysis are explored using grammars for harmony; both a hybrid system and a translation of limited context-free grammars into SEQ patterns have been developed. Finally, a distributed scenario is evaluated where SEQ and MEO are used in connection with DBpedia, following the Linked Data approach. The results show that applications are already possible and will benefit in the future from improved quality and compatibility of data sources as the Semantic Web evolves

Supporting software processes analysis and decision-making using provenance data

Data provenance can be defined as the description of the origins of a piece of data and the process by which it arrived in a database. Provenance has been successfully used in health sciences, chemical industries, and scientific computing, considering that these areas require a comprehensive traceability mechanism. Moreover, companies have been increasing the amount of data they collect from their systems and processes, considering the dropping cost of memory and storage technologies in the last years. Thus, this thesis investigates if the use of provenance models and techniques can support software processes execution analysis and data-driven decision-making, considering the increasing availability of process data provided by companies. A provenance model for software processes was developed and evaluated by experts in process and provenance area, in addition to an approach for capturing, storing, inferencing of implicit information, and visualization to software process provenance data. In addition, a case study using data from industry’s processes was conducted to evaluate the approach, with a discussion about several specific analysis and data-driven decision-making possibilities.Proveniência de dados é definida como a descrição da origem de um dado e o processo pelo qual este passou até chegar ao seu estado atual. Proveniência de dados tem sido usada com sucesso em domínios como ciências da saúde, indústrias químicas e computação científica, considerando que essas áreas exigem um mecanismo abrangente de rastreabilidade. Por outro lado, as empresas vêm aumentando a quantidade de dados que coletam de seus sistemas e processos, considerando a diminuição no custo das tecnologias de memória e armazenamento nos últimos anos. Assim, esta tese investiga se o uso de modelos e técnicas de proveniência é capaz de apoiar a análise da execução de processos de software e a tomada de decisões baseada em dados, considerando a disponibilização cada vez maior de dados relativos a processos pelas empresas. Um modelo de proveniência para processos de software foi desenvolvido e avaliado por especialistas em processos e proveniência, além de uma abordagem e ferramental de apoio para captura, armazenamento, inferência de novas informações e posterior análise e visualização dos dados de proveniência de processos. Um estudo de caso utilizando dados de processos da indústria foi conduzido para avaliação da abordagem e discussão de possibilidades distintas para análise e tomada de decisão orientada por estes dados

Ontology-based context-aware model for event processing in an IoT environment

The Internet of Things (IoT) is more and more becoming one of the fundamental sources of data. The observations produced by these sources are made accessible with heterogeneous vocabularies, models and data formats. The heterogeneity factor in such an enormous environment complicates the task of sharing and reusing this data in a more intelligent way (other than the purposes it was initially set up for). In this research, we investigate these challenges, considering how we can transform raw sensor data into a more meaningful information. This raw data will be modelled using ontology-based information that is accessible through continuous queries for sensor streaming data.Interoperability among heterogeneous entities is an important issue in an IoT environment. Semantic modelling is a key element to support interoperability. Most of the current ontologies for IoT mainly focus on resources and services information. This research builds upon the current state-of-the-art ontologies to provide contextual information and facilitate sensor data querying. In this research, we present an Ontology to represent an IoT environment, with emphasis on temporal and geospatial context enrichment. Furthermore, the Ontology is used alongside a proposed syntax based on Description Logic to build an Event Processing Model. The aim of this model is to interconnect ontology-based reasoning with event processing. This model enables to perform event processing over high-level ontological concepts.The Ontology was developed using the NeOn methodology, which emphasises on the reuse and modularisation. The Competency Questions techniques was used to develop the requirements of this Ontology. This was later evaluated by domain experts in software engineering and cloud computing. The ontology was evaluated based on its completeness, conciseness, consistency and expandability, over 70% of the domain experts agreed on the core modules, concepts and relationships within the ontology. The resulted Ontology provides a core IoT ontology that could be used for further development within a specific IoT domain. IIThe proposed Ontology-Based Context-Aware model for Event-Processing in an IoT environment “OCEM-IoT”, implements all the time operators used in complex event processing engines. Throughput and latency were used as performance comparison metrics for the syntax evaluation; the results obtained show an improved performance over existing event processing languages

Semi-automatic generation of learning domain modules for technology supported learning systems

In a time when Technology Supported Learning Systems are being widely used, there is a lack of tools that allows their development in an automatic or semi-automatic way. Technology Supported Learning Systems require an appropriate Domain Module, ie. the pedagogical representation of the domain to be mastered, in order to be effective. However, content authoring is a time and effort consuming task, therefore, efforts in automatising the Domain Module acquisition are necessary.Traditionally, textbooks have been used as the main mechanism to maintain and transmit the knowledge of a certain subject or domain. Textbooks have been authored by domain experts who have organised the contents in a means that facilitate understanding and learning, considering pedagogical issues.Given that textbooks are appropriate sources of information, they can be used to facilitate the development of the Domain Module allowing the identification of the topics to be mastered and the pedagogical relationships among them, as well as the extraction of Learning Objects, ie. meaningful fragments of the textbook with educational purpose.Consequently, in this work DOM-Sortze, a framework for the semi-automatic construction of Domain Modules from electronic textbooks, has been developed. DOM-Sortze uses NLP techniques, heuristic reasoning and ontologies to fulfill its work. DOM-Sortze has been designed and developed with the aim of automatising the development of the Domain Module, regardless of the subject, promoting the knowledge reuse and facilitating the collaboration of the users during the process

Chord sequence patterns in OWL

This thesis addresses the representation of, and reasoning on, musical knowledge in the Semantic Web. The Semantic Web is an evolving extension of the World Wide Web that aims at describing information that is distributed on the web in a machine-processable form. Existing approaches to modelling musical knowledge in the context of the Semantic Web have focused on metadata. The description of musical content and reasoning as well as integration of content descriptions and metadata are yet open challenges. This thesis discusses the possibilities of representing musical knowledge in the Web Ontology Language (OWL) focusing on chord sequence representation and presents and evaluates a newly developed solution. The solution consists of two main components. Ontological modelling patterns for musical entities such as notes and chords are introduced in the (MEO) ontology. A sequence pattern language and ontology (SEQ) has been developed that can express patterns in a form resembling regular expressions. As MEO and SEQ patterns both rewrite to OWL they can be combined freely. Reasoning tasks such as instance classification, retrieval and pattern subsumption are then executable by standard Semantic Web reasoners. The expressiveness of SEQ has been studied, in particular in relation to grammars. The complexity of reasoning on SEQ patterns has been studied theoretically and empirically, and optimisation methods have been developed. There is still great potential for improvement if specific reasoning algorithms were developed to exploit the sequential structure, but the development of such algorithms is outside the scope of this thesis. MEO and SEQ have also been evaluated in several musicological scenarios. It is shown how patterns that are characteristic of musical styles can be expressed and chord sequence data can be classified, demonstrating the use of the language in web retrieval and as integration layer for different chord patterns and corpora. Furthermore, possibilities of using SEQ patterns for harmonic analysis are explored using grammars for harmony; both a hybrid system and a translation of limited context-free grammars into SEQ patterns have been developed. Finally, a distributed scenario is evaluated where SEQ and MEO are used in connection with DBpedia, following the Linked Data approach. The results show that applications are already possible and will benefit in the future from improved quality and compatibility of data sources as the Semantic Web evolves.EThOS - Electronic Theses Online ServiceGBUnited Kingdo