Improving interoperability on industrial standards through ontologies

Interoperability refers to the effective exchange of information and understanding to collectively pursue common objectives. System developers commonly use ontologies to enhance semantic and syntactic interoperability within this context. This work aims to evaluate the contribution of ontology in making explicit the meaning of the entities described in a Piping and Instrumentation Diagram (P&ID) model and to provide an architecture that allows the representation of a P&ID in ontological knowledge bases. To understand the semantics of the P&ID entities and relations, we map each class of the P&ID to the corresponding entity of the Offshore Petroleum Production Plant Ontology (O3PO). The ontology describes the definition of each vocable associated with the axioms that clarify and regulate the meaning and utilization of this vocabulary. We intend to guarantee that the integration of P&ID with other models respects the original semantics and avoids unintended data exchanges. We follow this ontological analysis with a case study of a model that conforms to the Data Exchange in the Process Industry (DEXPI) specification, intended to provide homogeneous data interchange between CAD systems from diverse vendors. The ontological analysis of the DEXPI P&ID specification, to build a relation with a well-founded ontology, raises a set of desirable properties for a model intended for use in interoperability. While achieving technical interoperability between DEXPI P&IDs and ontologies represented in OWL is evident, we identified several challenges within the realm of semantic interoperability, specifically concerning clarity/intelligibility, conciseness, extendibility, consistency, and essence. These issues present significant hurdles to achieving seamless systems integration. Moreover, if the DEXPI standard were to evolve into a de facto standard for representing P&IDs across a broader range of domains than initially intended, these highlighted issues could potentially bottleneck its adoption and hinder its integration into different systems.Interoperabilidade se refere à troca efetiva de informação e entendimento na busca por objetivos comuns. Neste contexto, desenvolvedores de sistemas comumente utilizam ontologias para aprimorar a interoperabilidade semântica e sintática. O objetivo deste trabalho é avaliar a contribuição da ontologia para tornar explícito o significado das entidades descritas em um modelo de Diagrama de Tubulação e Instrumentação (DTI) e fornecer uma arquitetura que permita a representação de um DTI em bases de conhecimento ontológicas. Para entender a semântica das entidades e relações do DTI, mapeamos cada classe do DTI para a entidade correspondente da Ontologia de Planta de Produção de Petróleo Offshore (O3PO). A ontologia descreve a definição de cada vocábulo associado com os axiomas que esclarecem e regulam o significado e a utilização desse vocabulário. Pretendemos garantir que a integração do DTI com outros modelos respeite a semântica original e, assim, evite trocas de dados não intencionais. Seguimos essa análise ontológica com um estudo de caso de um modelo que se conforma à especificação "Data Exchange in the Process Industry" (DEXPI), destinada a fornecer uma troca de dados homogênea entre sistemas CAD de diversos fabricantes. A análise ontológica da especificação DEXPI DTI, para construir uma relação com uma ontologia bem fundamentada, levanta um conjunto de propriedades desejáveis para um modelo destinado a ser usado na interoperabilidade. Embora a conquista da interoperabilidade técnica entre DTIs DEXPI e ontologias representadas em OWL seja evidente, diversos desafios foram identificados no âmbito da interoperabilidade semântica, especificamente em relação à clareza/inteligibilidade, concisão, extensibilidade, consistência e essência. Essas questões representam obstáculos significativos para alcançar uma integração de sistemas perfeita. Além disso, se o padrão DEXPI evoluir para um padrão de facto para a representação de DTIs em um conjunto mais amplo de domínios do que inicialmente pretendido, essas questões destacadas poderiam potencialmente atrasar sua adoção e dificultar sua integração em sistemas diferentes

Data integration support for offshore decommissioning waste management

Offshore oil and gas platforms have a design life of about 25 years whereas the techniques and tools used for managing their data are constantly evolving. Therefore, data captured about platforms during their lifetimes will be in varying forms. Additionally, due to the many stakeholders involved with a facility over its life cycle, information representation of its components varies. These challenges make data integration difficult. Over the years, data integration technology application in the oil and gas industry has focused on meeting the needs of asset life cycle stages other than decommissioning. This is the case because most assets are just reaching the end of their design lives. Currently, limited work has been done on integrating life cycle data for offshore decommissioning purposes, and reports by industry stakeholders underscore this need. This thesis proposes a method for the integration of the common data types relevant in oil and gas decommissioning. The key features of the method are that it (i) ensures semantic homogeneity using knowledge representation languages (Semantic Web) and domain specific reference data (ISO 15926); and (ii) allows stakeholders to continue to use their current applications. Prototypes of the framework have been implemented using open source software applications and performance measures made. The work of this thesis has been motivated by the business case of reusing offshore decommissioning waste items. The framework developed is generic and can be applied whenever there is a need to integrate and query disparate data involving oil and gas assets. The prototypes presented show how the data management challenges associated with assessing the suitability of decommissioned offshore facility items for reuse can be addressed. The performance of the prototypes show that significant time and effort is saved compared to the state-of‐the‐art solution. The ability to do this effectively and efficiently during decommissioning will advance the oil the oil and gas industry’s transition toward a circular economy and help save on cost

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved ‘‘by using or adapting solutions to old problems.’’ In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most nonnumeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems

Total Constraint Management for Improving Construction Work Flow in Liquefied Natural Gas Industry

Australia has benefited and will continue to benefit significantly from Liquefied Natural Gas (LNG) investments underway. Managing these LNG projects is challenging as they become increasingly complex and technologically demanding. The primary goal of this thesis is to develop a Total Constraint Management (TCM) method to improve construction work flow during LNG construction. Five controlled experiments were conducted and results show that successful implementation of TCM can significantly improve construction productivity and reduce schedule overruns

Ontology-based solutions for interoperability among product lifecycle management systems: A systematic literature review

During recent years, globalization has had an impact on the competitive capacity of industries, forcing them to integrate their productive processes with other, geographically distributed, facilities. This requires the information systems that support such processes to interoperate. Significant attention has been paid to the development of ontology-based solutions, which are meant to tackle issues from inconsistency to semantic interoperability and knowledge reusability. This paper looks into how the available technology, models and ontology-based solutions might interact within the manufacturing industry environment to achieve semantic interoperability among industrial information systems. Through a systematic literature review, this paper has aimed to identify the most relevant elements to consider in the development of an ontology-based solution and how these solutions are being deployed in industry. The research analyzed 54 studies in alignment with the specific requirements of our research questions. The most relevant results show that ontology-based solutions can be set up using OWL as the ontology language, Protégé as the ontology modeling tool, Jena as the application programming interface to interact with the built ontology, and different standards from the International Organization for Standardization Technical Committee 184, Subcommittee 4 or 5, to get the foundational concepts, axioms, and relationships to develop the knowledge base. We believe that the findings of this study make an important contribution to practitioners and researchers as they provide useful information about different projects and choices involved in undertaking projects in the field of industrial ontology application.Fil: Fraga, Alvaro Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Vegetti, Maria Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Leone, Horacio Pascual. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

A Semantic Similarity Method for Products and Processes

豊橋技術科学大

An ontology-based data integration framework for construction information management

Information management during the construction phase of a built asset involves multiple stakeholders using multiple software applications to generate and store data. This is problematic as data comes in different forms and is labour intensive to piece together. Existing solutions to this problem are predominantly in proprietary applications, which are sometimes cost prohibitive for small engineering firms; or conceptual studies with use cases that cannot be easily adapted. In view of these limitations, this research presents an ontology-based data integration framework that makes use of open source tools that support Semantic Web technologies. The proposed framework enables: rapid answering of queries over construction data integrated from heterogeneous sources; data quality checks; and reuse of project software resources. The attributes and functionalities of the proposed solution align with the requirements common to small firms with limited information technology skill and budget. Consequently, this solution can be of great benefit for their data projects

Semantically defined Analytics for Industrial Equipment Diagnostics

In this age of digitalization, industries everywhere accumulate massive amount of data such that it has become the lifeblood of the global economy. This data may come from various heterogeneous systems, equipment, components, sensors, systems and applications in many varieties (diversity of sources), velocities (high rate of changes) and volumes (sheer data size). Despite significant advances in the ability to collect, store, manage and filter data, the real value lies in the analytics. Raw data is meaningless, unless it is properly processed to actionable (business) insights. Those that know how to harness data effectively, have a decisive competitive advantage, through raising performance by making faster and smart decisions, improving short and long-term strategic planning, offering more user-centric products and services and fostering innovation. Two distinct paradigms in practice can be discerned within the field of analytics: semantic-driven (deductive) and data-driven (inductive). The first emphasizes logic as a way of representing the domain knowledge encoded in rules or ontologies and are often carefully curated and maintained. However, these models are often highly complex, and require intensive knowledge processing capabilities. Data-driven analytics employ machine learning (ML) to directly learn a model from the data with minimal human intervention. However, these models are tuned to trained data and context, making it difficult to adapt. Industries today that want to create value from data must master these paradigms in combination. However, there is great need in data analytics to seamlessly combine semantic-driven and data-driven processing techniques in an efficient and scalable architecture that allows extracting actionable insights from an extreme variety of data. In this thesis, we address these needs by providing: • A unified representation of domain-specific and analytical semantics, in form of ontology models called TechOnto Ontology Stack. It is highly expressive, platform-independent formalism to capture conceptual semantics of industrial systems such as technical system hierarchies, component partonomies etc and its analytical functional semantics. • A new ontology language Semantically defined Analytical Language (SAL) on top of the ontology model that extends existing DatalogMTL (a Horn fragment of Metric Temporal Logic) with analytical functions as first class citizens. • A method to generate semantic workflows using our SAL language. It helps in authoring, reusing and maintaining complex analytical tasks and workflows in an abstract fashion. • A multi-layer architecture that fuses knowledge- and data-driven analytics into a federated and distributed solution. To our knowledge, the work in this thesis is one of the first works to introduce and investigate the use of the semantically defined analytics in an ontology-based data access setting for industrial analytical applications. The reason behind focusing our work and evaluation on industrial data is due to (i) the adoption of semantic technology by the industries in general, and (ii) the common need in literature and in practice to allow domain expertise to drive the data analytics on semantically interoperable sources, while still harnessing the power of analytics to enable real-time data insights. Given the evaluation results of three use-case studies, our approach surpass state-of-the-art approaches for most application scenarios.Im Zeitalter der Digitalisierung sammeln die Industrien überall massive Daten-mengen, die zum Lebenselixier der Weltwirtschaft geworden sind. Diese Daten können aus verschiedenen heterogenen Systemen, Geräten, Komponenten, Sensoren, Systemen und Anwendungen in vielen Varianten (Vielfalt der Quellen), Geschwindigkeiten (hohe Änderungsrate) und Volumina (reine Datengröße) stammen. Trotz erheblicher Fortschritte in der Fähigkeit, Daten zu sammeln, zu speichern, zu verwalten und zu filtern, liegt der eigentliche Wert in der Analytik. Rohdaten sind bedeutungslos, es sei denn, sie werden ordnungsgemäß zu verwertbaren (Geschäfts-)Erkenntnissen verarbeitet. Wer weiß, wie man Daten effektiv nutzt, hat einen entscheidenden Wettbewerbsvorteil, indem er die Leistung steigert, indem er schnellere und intelligentere Entscheidungen trifft, die kurz- und langfristige strategische Planung verbessert, mehr benutzerorientierte Produkte und Dienstleistungen anbietet und Innovationen fördert. In der Praxis lassen sich im Bereich der Analytik zwei unterschiedliche Paradigmen unterscheiden: semantisch (deduktiv) und Daten getrieben (induktiv). Die erste betont die Logik als eine Möglichkeit, das in Regeln oder Ontologien kodierte Domänen-wissen darzustellen, und wird oft sorgfältig kuratiert und gepflegt. Diese Modelle sind jedoch oft sehr komplex und erfordern eine intensive Wissensverarbeitung. Datengesteuerte Analysen verwenden maschinelles Lernen (ML), um mit minimalem menschlichen Eingriff direkt ein Modell aus den Daten zu lernen. Diese Modelle sind jedoch auf trainierte Daten und Kontext abgestimmt, was die Anpassung erschwert. Branchen, die heute Wert aus Daten schaffen wollen, müssen diese Paradigmen in Kombination meistern. Es besteht jedoch ein großer Bedarf in der Daten-analytik, semantisch und datengesteuerte Verarbeitungstechniken nahtlos in einer effizienten und skalierbaren Architektur zu kombinieren, die es ermöglicht, aus einer extremen Datenvielfalt verwertbare Erkenntnisse zu gewinnen. In dieser Arbeit, die wir auf diese Bedürfnisse durch die Bereitstellung: • Eine einheitliche Darstellung der Domänen-spezifischen und analytischen Semantik in Form von Ontologie Modellen, genannt TechOnto Ontology Stack. Es ist ein hoch-expressiver, plattformunabhängiger Formalismus, die konzeptionelle Semantik industrieller Systeme wie technischer Systemhierarchien, Komponenten-partonomien usw. und deren analytische funktionale Semantik zu erfassen. • Eine neue Ontologie-Sprache Semantically defined Analytical Language (SAL) auf Basis des Ontologie-Modells das bestehende DatalogMTL (ein Horn fragment der metrischen temporären Logik) um analytische Funktionen als erstklassige Bürger erweitert. • Eine Methode zur Erzeugung semantischer workflows mit unserer SAL-Sprache. Es hilft bei der Erstellung, Wiederverwendung und Wartung komplexer analytischer Aufgaben und workflows auf abstrakte Weise. • Eine mehrschichtige Architektur, die Wissens- und datengesteuerte Analysen zu einer föderierten und verteilten Lösung verschmilzt. Nach unserem Wissen, die Arbeit in dieser Arbeit ist eines der ersten Werke zur Einführung und Untersuchung der Verwendung der semantisch definierten Analytik in einer Ontologie-basierten Datenzugriff Einstellung für industrielle analytische Anwendungen. Der Grund für die Fokussierung unserer Arbeit und Evaluierung auf industrielle Daten ist auf (i) die Übernahme semantischer Technologien durch die Industrie im Allgemeinen und (ii) den gemeinsamen Bedarf in der Literatur und in der Praxis zurückzuführen, der es der Fachkompetenz ermöglicht, die Datenanalyse auf semantisch inter-operablen Quellen voranzutreiben, und nutzen gleichzeitig die Leistungsfähigkeit der Analytik, um Echtzeit-Daten-einblicke zu ermöglichen. Aufgrund der Evaluierungsergebnisse von drei Anwendungsfällen Übertritt unser Ansatz für die meisten Anwendungsszenarien Modernste Ansätze