Toward the automation of business process ontology generation

Semantic Business Process Management (SBPM) utilises semantic technologies (e.g., ontology) to model and query process representations. There are times in which such models must be reconstructed from existing textual documentation. In this scenario the automated generation of ontological models would be preferable, however current methods and technology are still not capable of automatically generating accurate semantic process models from textual descriptions. This research attempts to automate the process as much as possible by proposing a method that drives the transformation through the joint use of a foundational ontology and lexico-semantic analysis. The method is presented, demonstrated and evaluated. The original dataset represents 150 business activities related to the procurement processes of a case study company. As the evaluation shows, the proposed method can accurately map the linguistic patterns of the process descriptions to semantic patterns of the foundational ontology to a high level of accuracy, however further research is required in order to reduce the level of human intervention, expand the method so as to recognise further patterns of the foundational ontology and develop a tool to assist the business process modeller in the semi-automated generation of process models

Exploiting a perdurantist foundational ontology and graph database for semantic data integration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The view of reality that is inherent to perdurantist philosophical ontologies, often termed four dimensional (4D) ontologies, has not been widely adopted within the mainstream of information system design practice. However, as the closed world of enterprise systems is opened to Internet scale Semantic Web and Open Data information sources, there is a need to better understand the semantics of both internal and external data and how they can be integrated. Philosophical foundational ontologies can help establish this understanding and there is, therefore, an emerging need to research how they can be applied to the problem of semantic data integration. Therefore, a prime objective of this research was to develop a framework through which to apply a 4D foundational ontology and a graph database to the problem of semantic data integration, and to assess the effectiveness of the approach. The research employed design science, a methodology which is applicable to undertaking research within information systems as it encompasses methods through which the research can be undertaken and the resultant artefacts evaluated. This methodology has a number of discrete stages: problem awareness; a core design-build-evaluate iterative cycle through which the research is conducted; and a conclusion stage. The design science research was conducted through the development of a number of artefacts, the prime being the 4D-Semantic Extract Load (4D-SETL) framework. The effectiveness of the framework was assessed by applying it to semantically interpret and integrate a number of large scale datasets and to instantiate a prototype graph database warehouse to persist the resultant ontology. A series of technical experiments confirmed that directly reflecting the model patterns of 4D ontology within a prototype data warehouse proved an effective means of both structuring and semantically integrating complex datasets and that the artefacts produced by 4D-SETL could function at scale. Through illustrative scenario, the effectiveness of the approach is described in relation to the ability of the framework to address a number of weaknesses in current approaches. Furthermore the major advantages of the 4D-SETL are elaborated; which include ability of the framework is to combine foundational, domain and instance level ontological models in a single coherent system that dispensed with much of the translation normally undertaken between conceptual, logical and physical data models. Additionally, adopting a perdurantist realist foundational ontology provided a clear means of establishing and maintaining the identity of physical objects as their constituent temporal and spatial parts unfold over the course of tim

SOWL QL: Querying Spatio - Temporal Ontologies in OWL

We introduce SOWL QL, a query language for spatio-temporal information in ontologies. Buildingupon SOWL (Spatio-Temporal OWL), an ontology for handling spatio-temporal information in OWL, SOWL QL supports querying over qualitative spatio-temporal information (expressed using natural language expressions such as “before”, “after”, “north of”, “south of”) rather than merely quantitative information (exact dates, times, locations). SOWL QL extends SPARQL with a powerful set of temporal and spatial operators, including temporal Allen topological, spatial directional and topological operations or combinations of the above. SOWL QL maintains simplicity of expression and also, upward and downward compatibility with SPARQL. Query translation in SOWL QL yields SPARQL queries implying that, querying spatio-temporal ontologies using SPARQL is still feasible but suffers from several drawbacks the most important of them being that, queries in SPARQL become particularly complicated and users must be familiar with the underlying spatio-temporal representation (the “N-ary relations” or the “4D-fluents” approach in this work). Finally, querying in SOWL QL is supported by the SOWL reasoner which is not part of the standard SPARQL translation. The run-time performance of SOWL QL has been assessed experimentally in a real data setting. A critical analysis of its performance is also presented

Reasoning in Common Data Environments. Re-thinking CDEs to enhance collaboration in BIM processes

In BIM processes, the concept of Common Data Environment - CDE - has often been depicted as a key element for successful collaboration and information sharing among different actors but, in current practice, acts as a mere documentation repository ineffective for true collaborative purposes. Therefore, the idea of CDE seems to be overrated on the one hand and unexploited on the other, while effective collaboration is still far from being decisively supported. To overcome this lack, the present research focuses on the definition of a new generation of CDEs, enhanced with an information level for knowledge integration provided by different information carriers such as models and datasets. The paper discusses its development through a graph database platform and dedicated methodologies for data retrieval and query, to verify coherence and consistency of information among different models

Improving model quality through foundational ontologies: Two contrasting approaches to the representation of roles

Several foundational ontologies have been developed recently. We examine two of these from the point of view of their quality in representing temporal changes, focusing on the example of roles. We discuss how these are modelled in two foundational ontologies: the Unified Foundational Ontology and the BORO foundational ontology. These exhibit two different approaches, endurantist and perdurantist respectively. We illustrate the differences using a running example in the university student domain, wherein one individual is not only a registered student but also, for part of this period, was elected the President of the Student Union. The metaphysical choices made by UFO and BORO lead to different representations of roles. Two key differences which affect the way roles are modelled are exemplified in this paper: (1) different criteria of identity and (2) differences in the way individual objects extend over time and possible worlds. These differences impact upon the quality of the models produced in terms of their respective explanatory power. The UFO model concentrates on the notion of validity in “all possible worlds” and is unable to accurately represent the way particulars are extended in time. The perdurantist approach is best able to describe temporal changes wherein roles are spatio-temporal extents of individuals

Visualisation of semantic architectural information within a game engine environment

Because of the importance of graphics and information within the domain of architecture, engineering and construction (AEC), an appropriate combination of visualisation technology and information management technology is of utter importance in the development of appropriately supporting design and construction applications. We therefore started an investigation of two of the newest developments in these domains, namely game engine technology and semantic web technology. This paper documents part of this research, containing a review and comparison of the most prominent game engines and documenting our architectural semantic web. A short test-case illustrates how both can be combined to enhance information visualisation for architectural design and construction

Towards a semantic Construction Digital Twin: directions for future research

As the Architecture, Engineering and Construction sector is embracing the digital age, the processes involved in the design, construction and operation of built assets are more and more influenced by technologies dealing with value-added monitoring of data from sensor networks, management of this data in secure and resilient storage systems underpinned by semantic models, as well as the simulation and optimisation of engineering systems. Aside from enhancing the efficiency of the value chain, such information-intensive models and associated technologies play a decisive role in minimising the lifecycle impacts of our buildings. While Building Information Modelling provides procedures, technologies and data schemas enabling a standardised semantic representation of building components and systems, the concept of a Digital Twin conveys a more holistic socio-technical and process-oriented characterisation of the complex artefacts involved by leveraging the synchronicity of the cyber-physical bi-directional data flows. Moreover, BIM lacks semantic completeness in areas such as control systems, including sensor networks, social systems, and urban artefacts beyond the scope of buildings, thus requiring a holistic, scalable semantic approach that factors in dynamic data at different levels. The paper reviews the multi-faceted applications of BIM during the construction stage and highlights limits and requirements, paving the way to the concept of a Construction Digital Twin. A definition of such a concept is then given, described in terms of underpinning research themes, while elaborating on areas for future research

Ontologias para Manutenção Preditiva com Dados sensíveis ao tempo

As empresas de fabrico industrial devem assegurar um processo produtivo contínuo para serem competitivas e fornecer os produtos fabricados no prazo e com a qualidade exigida pelos clientes. A quebra da cadeia de fabrico pode ter desfechos graves, resultando numa redução da produção e na interrupção da cadeia de abastecimento. Estes processos são compostos por cadeias de máquinas que executam tarefas em etapas. Cada máquina tem uma tarefa específica a executar, e o resultado de cada etapa é fornecido à próxima etapa. Uma falha imprevista numa das máquinas tende a interromper toda a cadeia produtiva. A manutenção preventiva agendada tem como objetivo evitar a ocorrência de falhas, tendo como base o tempo médio antes da falha (MTBF), que representa a expectativa média de vida de componentes individuais com base em dados históricos. As tarefas de manutenção podem implicar um período de paralisação e a interrupção da produção. Esta manutenção é executada rotineiramente e a substituição de componentes não considera a necessidade premente da sua substituição, sendo os mesmos substituídos com base no ciclo do agendamento. É aqui que a manutenção preditiva é aplicável. Efetuando a recolha de dados de sensores dos equipamentos, é possível detetar irregularidades nos dados recolhidos, através da aplicação de processos de raciocínio e inferência, conduzindo à atempada previsão e deteção de falhas. Levando este cenário à otimização do tempo de manutenção, evitando falhas inesperadas, à redução de custos e ao aumento da produtividade em comparação com a manutenção preventiva. Os dados fornecidos pelos sensores são sensíveis ao tempo, variações e flutuações ocorrem ao longo do tempo e devem ser analisados em relação ao período em que ocorrem. Esta dissertação tem como objetivo o desenvolvimento de uma ontologia para a manutenção preditiva que descreva a sua abrangência e o campo da sua aplicação. A aplicabilidade da ontologia será demonstrada com uma ferramenta, igualmente desenvolvida, que transforma dados sensíveis ao tempo recolhidos em tempo real a partir de sensores de máquinas industriais, fornecidos por WebServices, em indivíduos dessa mesma ontologia, considerando a representação do fator temporal dos dados.Manufacturing companies must ensure a continuous production process to be competitive and supply the manufactured goods in time and with the desired quality the customers expect. Any disruption in the manufacturing chain may have disastrous consequences, representing a shortage of production and the interruption of the supply chain. The manufacturing processes are composed of a chain of industrial machines operating in stages. Each machine has a specific task to complete, and the result of each stage is forwarded to the next stage. An unpredicted malfunction of one of the machines tends to interrupt the whole production chain. Scheduled Preventive maintenance intends to avoid causes leading to faults, but relies on parameters such as Mean Time Before Failure (MTBF), which represents the average expected life span of individual components based on statistical data. A maintenance task may lead to a period of downtime and consequently to a production halt. Being the maintenance scheduled and executed routinely, the replacement of components, does not consider the effective need of its replacement, they are replaced based on the scheduling cycle. This is where predictive maintenance is applicable. By collecting sensor data of industrial equipment, anomalies can be determined through reasoning and inference processes applied to the data, leading to an early fault and time to failure prediction. This scenario leads to maintenance timing optimization, avoidance of unexpected failures, cost savings and improved productivity when compared to preventive maintenance. Data supplied by sensors is timesensitive, as variations and fluctuations occur over periods of time and must be analysed concerning the period they occur. This dissertation aims to develop an ontology for predictive maintenance that describes the scope and field of application. The applicability of the ontology will be demonstrated with a tool, also to be developed, that transforms time-sensitive data collected in real time from sensors of industrial machines, provided by a WebServices, into individuals of the same ontology, considering the representation of the temporal factor of the data