Gulnar Mehdi

Capturing Industrial Information Models with Ontologies and Constraints

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are often used in industry to capture the conceptual information models underpinning applications. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance.

Semantic access to streaming and static data at Siemens

We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

Towards Semantic Reasoning in Knowledge Management Systems

Modern applications of AI systems rely on their ability to acquire, represent and process expert knowledge for problem-solving and reasoning. Consequently, there has been significant interest in both industry and academia to establish advanced knowledge management (KM) systems, promoting the effective use of knowledge. In this paper, we examine the requirements and limitations of current commercial KM systems and propose a new approach to semantic reasoning supporting Big Data access, analytics, reporting and automation related tasks. We also provide comparative analysis of how state-of-the-art KM systems can benefit from semantics by illustrating examples from the life-sciences and industry. Lastly, we present results of our semantic-based analytics workflow implemented for Siemens power generation plants.

Semantic Rule-Based Equipment Diagnostics

Industrial rule-based diagnostic systems are often data-dependant in the sense that they rely on specific characteristics of individual pieces of equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. In this work we address these problems by relying on Ontology-Based Data Access: we use ontologies to mediate the equipment and the rules. We propose a semantic rule language, sigRL, where sensor signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system, deployed it in Siemens, and conducted experiments to demonstrate both usability and efficiency.

Semantic Rules for Machine Diagnostics: Execution and Management

Rule-based diagnostics of equipment is an important task in industry. In this paper we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages used in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We study computational complexity of SigRL: execution of diagnostic programs, provenance computation, as well as automatic verification of redundancy and inconsistency in diagnostic programs.

SemDia: Semantic Rule-Based Equipment Diagnostics Tool

Rule-based diagnostics of power generating equipment is an important task in industry. In this demo we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We present our diagnostic system SemDia. The attendees will be able to write diagnostic programs in SemDia using sigRL over 50 Siemens turbines. We also present how such programs can be automatically verified for redundancy and inconsistency. Moreover, the attendees will see the provenance service that SemDia provides to trace the origin of diagnostic results.

Diagnostics of Trains with Semantic Diagnostics Rules

Industry today employs rule-based diagnostic systems to minimize the maintenance cost and downtime of equipment. Rules are typically used to process signals from sensors installed in equipment by filtering, aggregating, and combining sequences of time-stamped measurements recorded by the sensors. Such rules are often data-dependent in the sense that they rely on specific characteristics of individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers especially when the rules require domain knowledge. In this work we propose an approach to address these problems by relying on the well-known Ontology-Based Data Access approach: we propose to use ontologies to mediate the sensor signals and the rules. To this end, we propose a semantic rule language, SDRL, where signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system and evaluated it. For evaluation we developed a use case of rail systems at Siemens and conducted experiments to demonstrate both usability and efficiency of our solution.

Ontology-based integration of performance related data and models: An application to industrial turbine analytics

In industrial power generation plants, subsystem monitoring and analytics play a vital role in quantifying the knowledge about different factors that impact their overall performance. Multi-dimensional performance metrics, e.g. thermal efficiency, in-service time, mean-time-to-failure etc., are calculated that may have different data constraints, modelling techniques, and execution frameworks. Automating these calculations and combining multiple metrics to form a single performance index (e.g. reliability) is a challenging task as it requires considerable domain-specific expertise and consolidation of performance-related data and its underlying models. In this paper, we propose to use ontologies to assist domain analyst to first, capture appropriate semantic data of an individual performance metric, and later to provide means to integrate and execute multiple metrics to accurately reflect the overall performance of a plant. We present our prototypical implementation, its evaluation; furthermore, we discuss an ontology model that currently describes three distinct analytical models and its related data based on the case study of Siemens gas turbines. We also demonstrate how ontologies can support to infer the appropriate aggregation method in calculating composite indices.