Probabilistic techniques for bridging the semantic gap in schema alignment

Connecting pieces of informations from heterogeneous sources sharing the same domain is an open challenge in Semantic Web, Big Data and business communities. The main problem in this research area is to bridge the expressiveness gap between relational databases and ontologies. In general, an ontology is more expressive and captures more semantic information behind data than a relational database does. On the other side, databases are the most common used persistent storage system and they grant benefits such as security and data integrity but they need to be managed by expert users. The problem is quite significant above all when enterprise or corporate ontologies are used to share infomations coming from different databases and where a more efficient data management is auspicable for interoperability purposes. The main motivations on this thesis are related to the database access via ontology, as in the OBDA (Ontology Based Data Access) scenario, wich provides a formal specification of the domain close to the human’s view, while technical details of the database are hidden from end-user, and also the persistent storageof ontologies in databases for facilitating search and retrieval, keeping the benefits of database management systems. In these cases the assertion component (A-Box) is usually stored into a database, and terminological one (T-Box) is mantained in an ontology. So it is more necessary to align schemas than matching instances. The term alignment can be used to define the whole process comprising the mapping process between two existent heterogeneous sources, such as ontology and relational database, and the trasformation process from a representation to the other one, such as ontology-to-database and database-to-ontology. Defining mappings manually is an hard task expecially for large and complex data representations and existing methodologies fail in loosing some contents and several elements are left unaligned. In this thesis are discussed various aspects of the alignment in all these senses. The presented techniques are based on a probabilistic approach that fits well on the uncertain alignment process, where are involved two different representations with a different level of expressiveness. In the methodology ontologies and databases are described in terms of Ontology Web Language (OWL) and Entity-Relationship Diagram (ERD) lexical descriptions. So, the ontologies are represented by a set of OWL axioms while a properly defined Context-Free Grammar (CFG) is used to represent ERDs (Entity-Relationship Diagrams) as a set of sentences. Both the OWL → ERD transformation and the mapping rely on HMMs (Hidden Markov Models) to estimate the most likely sequence of ERD symbols observing OWL symbols. In the model definition OWL constructs are the observable states, while the ERD symbols are the hidden states. The tools developed, one for OWL → ERD transformation purpose, called OMEGA (Ontology → Markov → ERD Generator Application) and one for mapping OWL and ERD, called HOwErd (HMM OWL-ERD) own their own GUI interface for showing the alignment results. Finally, HOwErd is compared with the most widespread tools in the reference literature

Semantic and Syntactic Matching of Heterogeneous e-Catalogues

In e-procurement, companies use e-catalogues to exchange product infor-mation with business partners. Matching e-catalogues with product requests helps the suppliers to identify the best business opportunities in B2B e-Marketplaces. But various ways to specify products and the large variety of e-catalogue formats used by different business actors makes it difficult. This Ph.D. thesis aims to discover potential syntactic and semantic rela-tionships among product data in procurement documents and exploit it to find similar e-catalogues. Using a Concept-based Vector Space Model, product data and its semantic interpretation is used to find the correlation of product data. In order to identify important terms in procurement documents, standard e-catalogues and e-tenders are used as a resource to train a Product Named Entity Recognizer to find B2B product mentions in e-catalogues. The proposed approach makes it possible to use the benefits of all availa-ble semantic resources and schemas but not to be dependent on any specific as-sumption. The solution can serve as a B2B product search system in e-Procurement platforms and e-Marketplaces

A Cooperative Approach for Composite Ontology Matching

Ontologies have proven to be an essential element in a range of applications in which knowl-edge plays a key role. Resolving the semantic heterogeneity problem is crucial to allow the interoperability between ontology-based systems. This makes automatic ontology matching, as an anticipated solution to semantic heterogeneity, an important, research issue. Many dif-ferent approaches to the matching problem have emerged from the literature. An important issue of ontology matching is to find effective ways of choosing among many techniques and their variations, and then combining their results. An innovative and promising option is to formalize the combination of matching techniques using agent-based approaches, such as cooperative negotiation and argumentation. In this thesis, the formalization of the on-tology matching problem following an agent-based approach is proposed. Such proposal is evaluated using state-of-the-art data sets. The results show that the consensus obtained by negotiation and argumentation represent intermediary values which are closer to the best matcher. As the best matcher may vary depending on specific differences of multiple data sets, cooperative approaches are an advantage. *** RESUMO - Ontologias são elementos essenciais em sistemas baseados em conhecimento. Resolver o problema de heterogeneidade semântica é fundamental para permitira interoperabilidade entre sistemas baseados em ontologias. Mapeamento automático de ontologias pode ser visto como uma solução para esse problema. Diferentes e complementares abordagens para o problema são propostas na literatura. Um aspecto importante em mapeamento consiste em selecionar o conjunto adequado de abordagens e suas variações, e então combinar seus resultados. Uma opção promissora envolve formalizara combinação de técnicas de ma-peamento usando abordagens baseadas em agentes cooperativos, tais como negociação e argumentação. Nesta tese, a formalização do problema de combinação de técnicas de ma-peamento usando tais abordagens é proposta e avaliada. A avaliação, que envolve conjuntos de testes sugeridos pela comunidade científica, permite concluir que o consenso obtido pela negociação e pela argumentação não é exatamente a melhoria de todos os resultados individuais, mas representa os valores intermediários que são próximo da melhor técnica. Considerando que a melhor técnica pode variar dependendo de diferencas específicas de múltiplas bases de dados, abordagens cooperativas são uma vantagem

Ontology-based knowledge management for technology intensive industries

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Managing knowledge for capability engineering

The enterprises that deliver capability are trying to evolve into through-life businesses by shifting away from the traditional pattern of designing and manufacturing successive generations of products, towards a new paradigm centred on support, sustainability and the incremental enhancements of existing capabilities from technology insertions and changes to process. The provision of seamless through-life customer solutions depends heavily on management of information and knowledge between, and within the different parts of the supply chain enterprise. This research characterised and described Capability Engineering (CE) as applied in the defence enterprise and identified to BAE Systems important considerations for managing knowledge within that context. The terms Capability Engineering and Through Life Capability Management (TLCM), used synonymously in this thesis, denote a complex evolving domain that requires new approaches to better understand the different viewpoints, models and practices. The findings and novelty of this research is demonstrated through the following achievements: Defined the problem space that Requirements Engineers can use in through-life management projects. Made a contribution to the development of models for Systems Architects to enable them to incorporate ‘soft’ systems within their consideration. Independently developed a TLCM activity model against which BAE Systems validated the BAE Systems TLCM activity model, which is now used by UK Ministry of Defence (MoD). Developed, and published within INCOSE1, the INCOSE Capability Engineering ontology. Through the novel analysis of a directly applicable case study, highlighted to Functional Delivery Managers the significance of avoiding the decoupling of information and knowledge in the context of TLCM. Through experimentation and knowledge gained within this research, identified inadequacies in the TechniCall (rapid access to experts) service which led to the generation of requirements for an improved service which is now being implemented by BAE Systems. The results showed that managing knowledge is distinct when compared to information management. Over-reliance on information management in the absence of tacit knowledge can lead to a loss in the value of the information, which can result in unintended consequences. Capability is realised through a combination of component systems and Capability Engineering is equivalent to a holistic perspective of Systems Engineering. A sector-independent Capability Engineering ontology is developed to enable semantic interoperability between different domains i.e. defence, rail and information technology. This helped to better understand the dependencies of contributing component systems within defence, and supported collaboration across different domains. Although the evaluation of the ontology through expert review has been accomplished; the ontology, KM analysis framework and soft systems transitioning approach developed still need to undergo independent verification and validation. This requires application to other case studies to check and exploit their suitability. This Engineering Doctorate research has been disseminated through a number of peer reviewed publications

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Social Learning Systems: The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems

In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians’ interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN\u27s). This work followed an independent effort to develop an evolutionary knowledge management system, and outlines a model for integrating the wisdom of the crowd into the process of collecting, analyzing, and curating data for dynamical knowledge systems. Throughout, we examine how relational data modeling, automated reasoning, crowdsourcing, and social curation techniques have been exploited to extend the utility of web-based, transactional knowledge management systems, creating a new breed of knowledge-based system in the process: the Social Learning System (SLS). The key questions this work has explored by way of elucidating the SLS model include considerations for 1) how it is possible to unify Web and OSN mining techniques to conform to a versatile, structured, and computationally-efficient ontological framework, and 2) how large-scale knowledge projects may incorporate tiered collaborative editing systems in an effort to elicit knowledge contributions and curation activities from a diverse, participatory audience