Abstract Knowledge Graph Embedding (KGE) has attracted increasing attention. Relation patterns, such as symmetry and inversion, have received considerable focus. Among them, composition patterns are particularly important, as they involve nearly all relations in KGs. However, prior KGE approaches often consider relations to be compositional only if they are well-represented in the training data. Consequently, it can lead to performance degradation, especially for under-represented composition patterns. To this end, we propose HolmE, a general form of KGE with its relation embedding space closed under composition, namely that the composition of any two given relation embeddings remains within the embedding space. This property ensures that every relation embedding can compose, or be composed by other relation embeddings. It enhances HolmE’s capability to model under-represented (also called long-tail) composition patterns with limited learning instances. To our best knowledge, our work is pioneering in discussing KGE with this property of being closed under composition. We provide detailed theoretical proof and extensive experiments to demonstrate the notable advantages of HolmE in modelling composition patterns, particularly for long-tail patterns. Our results also highlight HolmE’s effectiveness in extrapolating to unseen relations through composition and its state-of-the-art performance on benchmark datasets.

Mapping identifiers across the systems is an essential part of the work towards automatic system integration. It will help us move from the traditional processes that are often manual, error-prone, and time and resource-consuming. In this paper, we propose a solution that will allow us to approach the problem from a data mesh perspective, where organisations and domains serve and host their own data for consumption, combined with ontology-based data access to move towards machine-readable data, more automatic data integration, and further digitalisation in engineering systems. This is an important step as structured and enhanced system-to-system data interchange has enormous efficiency gains and consequential savings in the oil and gas industry, as well as in other sectors of engineering with similar challenges.

Monitoring of the state, performance, quality of operations and other parameters of equipment and production processes, which is typically referred to as condition monitoring, is an important common practice in many industries including manufacturing, oil and gas, chemical and process industry. In the age of Industry 4.0, where the aim is a deep degree of production automation, unprecedented amounts of data are generated by equipment and processes, and this enables adoption of Machine Learning (ML) approaches for condition monitoring. Development of such ML models is challenging. On the one hand, it requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers with asymmetric backgrounds. On the other hand, there is high variety and diversity of data relevant for condition monitoring. Both factors hampers ML modelling for condition monitoring. In this work, we address these challenges by empowering ML-based condition monitoring with semantic technologies. To this end we propose a software system SemML that allows to reuse and generalise ML pipelines for conditions monitoring by relying on semantics. In particular, SemML has several novel components and relies on ontologies and ontology templates for ML task negotiation and for data and ML feature annotation. SemML also allows to instantiate parametrised ML pipelines by semantic annotation of industrial data. With SemML, users do not need to dive into data and ML scripts when new datasets of a studied application scenario arrive. They only need to annotate data and then ML models will be constructed through the combination of semantic reasoning and ML modules. We demonstrate the benefits of SemML on a Bosch use-case of electric resistance welding with very promising results.

Digital twins (DTs) are a powerful mechanism for representing complex industrial assets such as oil platforms as digital models. These models can facilitate temporal analyses and computer simulations of assets. In order to enable this, DTs should be able to capture characteristics of an asset as specified by the manufacturer, its state during the run time, as well as how the asset interacts with other assets in a complex system. We argue that semantic technologies and in particular semantic models or ontologies is promising modelling paradigm for DTs. Semantic models allow to capture complex systems in an intuitive fashion, can be written in standardised ontology languages, and come with a wide range of off-the-shelf systems to design, maintain, query, and navigate semantic models. In this work we report our preliminary results on developing a system that would support semantic-based DTs. In particular, we plan to augment the PI System developed by OSIsoft with ontologies and show how the resulting solution can help in simplifying analytical and machine learning routines for DTs.

Analytical workflows are heavily used in large and data intensive companies. An important application of such workflows in Siemens is equipment analytics when equipment KPIs and reports are computed by aggregating equipment's operational, master, and analytical data. In Siemens this data satisfies big data dimensions and this dependence poses significant challenges in authoring, reuse, and maintenance of analytical workflows by engineers and data scientists. In this work we propose to address these problems by relying on semantic technologies: we use ontologies to give a high level representation of equipment's operational and master data and offer a high level language to express KPIs over ontologies. We implemented our approach, integrated it with KNIME, and evaluated at Siemens. This is a preliminary work and we are excited about its further extensions.

The competitiveness of modern enterprises heavily depends on their ability to make the right business decisions by relying on efficient and timely analysis of the right business critical data. In large and data intensive companies such as Equinor, a Norwegian multinational oil and gas company with more than 20,000 employees, gathering such data is not a trivial task due to the growing size and complexity of corporate information sources. As a result, the data gathering task is often the most time-consuming part of the decision making process, in particular when it comes to the work processes of Equinor’s exploration geologists that should find in a timely manner new exploitable accumulations of oil or gas in given areas by analysing data about these areas. In this work we present our experience in addressing this data challenge tast at Equinor. We have developed and deployed at Equinor a semantic data access system that relies on the Ontology Based Data Access (OBDA) approach. Our system is based on our solid theoretical contributions and has been extensively evaluated at Equinor.

In the database community Polystores is an emerging and promising approach for data federation that aims at designing a unified querying layer over multiple data models. In the Semantic Web community a similar in spirit approach of Ontology-Based Data Access (OBDA) has been recently proposed, attracted a lot of attention, and proved its success in several industrial scenarios. In this paper we discuss a semantic approach to building polystores using the OBDA paradigm. We also present our system Optique that is utilized in an industrial application of performing turbine diagnostics in Siemens.