In this demo we present the SOMM system that resulted from an ongoing collaboration between Siemens and the University of Oxford. The goal of this collaboration is to facilitate design and management of ontologies that capture conceptual information models underpinning various industrial applications. SOMM supports engineers with little background on semantic technologies in the creation of such ontologies 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 ontology integration. We demonstrate functionality of SOMM on two scenarios from energy and manufacturing domains.
Software systems in the domain of industrial manufacturing have become increasingly important in recent years. Research in this area has highlighted the need for enterprise-wide information models |machine-readable conceptualisations describing the functionality of and information ow between the di erent assets in a plant, such as equipment and production processes. The development information models based on ISA and IEC standards has now become a common practice in modern companies.
A number of companies in the manufacturing industry, including Siemens,
exploit information models in deployed applications [
In order to address this challenge, semantic technologies have been recently
adapted in industry to formalise information models using ontologies [5{7]. In
particular, OWL 2 provides a rich and exible modelling language for describing
y The demo is accompanying our ISWC'16 paper [
In this paper, we present the Siemens-Oxford Model Manager (SOMM) that
was developed to facilitate development and maintenance of ontology-based
industrial information models and resulted from an ongoing collaboration between
Siemens CT in Munich and the University of Oxford. SOMM has been designed
to support engineers with little background on semantic technologies in the
creation and use of ontologies. SOMM provides a simple interface for ontology
development and enables the introduction of instance data via automatically
generated forms that are driven by the ontology and which help minimising
errors in data entry. SOMM implements a fragment of the OWL 2 RL pro le
extended with database integrity constraints for data validation. SOMM is built
on top of Web-Protege [
We showcase SOMM using two industrial scenarios and for both of them we have developed ontologies using SOMM. The rst ontology describes the manufacturing processes in a plant, i.e., it represents equipment, materials, as well as processes and how they are connected with each other. The second one describes the structure of power generating turbines, their functionality, and operational modes. During the demonstration the attendees will be able to modify these ontologies and to try SOMM's constraint validation and query answering functionality over realistic manufacturing and gas turbine data. 2
System Overview. SOMM is built on top of the Web-Protege platform by extending its front-end with new visual components and its back-end by connecting it to IRIS, HermiT and LogMap. Our choice of WebProtege was based on the requirements for the platform underpinning SOMM that are natural interdisciplinary requirements of engineering environments and were also obtained during the use-case study at Siemens. That is, the platform should be (i) accesible as a Web application; (ii) under active development; (iii) open-source and modular; (iv) with built-in functionality for ontology versioning and collaborative development; (v) o ering a form-based and end-user oriented interface; and (vi) o ering automatic form generation for data insertion.
Ontology and Constraint Language of SOMM. Based on the analyses of ISA and
IEC standards as well as on Siemens requirements to industrial modelling we did
several modelling choices underpinning the design of our ontologies and identify a
fragment of OWL 2 RL that is su cient to capture the basic aspects of the Siemens'
information models. In particular, this fragment allows to express obligatory
relationships between (pairs of) entities using SubClassOf, SubDataPropertyOf,
TransitiveObjectProperty, and InverseObjectProperties. These axioms can be
readily exploited by reasoners to support query answering. Additionally, we
allow to express optional relationships between entities by using AllValuesFrom.
Our analysis of the models, however, also revealed the need to incorporate
database integrity constraints for data validation, which are not supported in
OWL 2. We express them using OWL 2 axioms that use ObjectSomeValuesFrom,
ObjectMinCardinality, and ObjectMaxCardinality while we treat them as integrity
constraints rather than axioms. Finally, for the purpose of data validation and
query answering we capture OWL 2 RL axioms and integrity constraints by
means of rules with strati ed negation. We refer the reader to [
SOMM Functionality. The interface of SOMM is restricted to support only the OWL 2 RL axioms and constraints discussed above. SOMM allows for insertion of axioms and constraints via a form-based interface for editing axioms and constraints, see Figure 1(1) for a screenshot of the SOMM class editor. SOMM can also automatically generate data forms, see Figure 1(2), by exploiting the capabilities of the `knowledge acquisition forms' in Web-Protege. The forms are automatically generated for each class based on its relevant mandatory and optional properties by considering the explicitly provided properties, the inherited properties, and the properties explicitly attached to its descendant classes. Moreover, SOMM supports reasoning by relying on the OWL 2 reasoner HermiT to support standard reasoning services such as class satis ability and ontology classi cation. Data validation and query answering support is provided on top of the IRIS engine. Figures 1(3) and 1(4) illustrate instance and class reasoning interfaces. In addition to classical subsumption hierarchies, SOMM allows also for hierarchies based on arbitrary properties. These can be seen as a generalisation of partonomy hierarchies. Figures 1(5) and 1(6) show the hierarchy corresponding to the follows property at both class and instance level. Finally, SOMM integrates the ontology alignment system LogMap to support model alignment and merging. Users can select and merge two available Web-Protege projects, or import and merge an ontology into the active Web-Protege project. 3
In order to demonstrate how to develop and manage industrial ontologies with SOMM and how to do data validation and query answering with SOMM, we developed a manufacturing and an energy generating scenarios. Each scenario consists of an ontology, data, and queries and we now describe them in details. Manufacturing. Conceptual models for manufacturing applications are typically based on the international standard ISA-88/95. Our manufacturing ontology re ects these models and contains 79 standard axioms and 20 constraints. It describes speci cations of products, processes and materials that can be used to manufacture these products, as well as the way these processes can be composed and executed. The data we generated simulates manufacturing of products of two types based on this ontology. We prepared data where some products were manufactured in violation of the ontology (e.g., they used too much material of some kind) and data where each product is manufactured according to the ontology. For querying the data we prepared three queries from the tracking-andtracing application that are commonly used in practice. The rst query asks for all products that use material from a given lot; the second asks for all material lots used in a given product; nally, the third one asks for the total quantity of material in lots of a speci c kind.
Gas Turbine. This ontology captures an energy plant model that is based on the Reference Designation System for Power Plants (RDS-PP) and KraftwerkKennzeichensysten (KKS) standards, which are in turn extensions for the energy sector of the IEC 81346 and ISO/TS 16952-10 international standards. The ontology contains 121 standard axioms and 25 constraints. The data we prepared is anonymised real data that describes the structure of 800 real gas turbines of di erent types, their sensor readings (temperature, pressure, rotor speed and position), and associated processes (e.g., expansion, compression, start up, shut down). The dataset was converted from a relational DB into RDF, and contains 25090 triples involving 4076 individuals. We prepared three test queries that are commonly used in practice. The rst query asks for the core parts, equipment and current state of all turbines of a given type; the second asks for all components involved in a compression process; the last query asks for the temperature readings of turbines of a given type.