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An Open Semantic Framework for the Industrial Internet of Things

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An Open Semantic Framework for the Industrial Internet of Things INTERNET OF THINGS Editor: Amit Sheth, Kno.e.sis at Wright State University, [email protected] An Open Semantic Framework for the Industrial Internet of Things Simon Mayer, Jack Hodges, Dan Yu, Mareike Kritzler, and Florian Michahelles, Siemens Corporate Technology riven by academia1 and fueled by govern- ing a product combined with machine-readable ment funding,2 research and development descriptions of production machines would enable D much more agile manufacturing in a mass- in the Internet of Things (IoT) initially focused on customized world. Thinking beyond vertical domain basic enabling technologies and architectures for silos,6 such manufacturing equipment could also the identifi cation, networking, and discovery of be semantically integrated with the smart grid, smart devices. Today, almost two decades later, which would unlock energy-saving potential by big players from a wide range of industries have autonomously shutting down idling machines. jumped on the bandwagon and base their future Scenarios such as these require the tedious task of business and growth prospects on the IoT. At transferring knowledge from human experts and the same time, industrial players, standardiza- representing it in a form that can be readily used tion organizations, and academia are converg- by autonomous systems.7 This is worth the invest- ing with respect to the IoT’s basic technologies. ment—once domain-specifi c ontologies have been To a large extent, the IoT has become synony- created and properly integrated, they will become mous with the Web of Things (WoT), which em- a semantic backbone that lets machines collabo- phasizes the reuse of proven standards from the rate with each other and with humans as never World Wide Web that lower barriers of entry, en- before. sure scalability, and have been scaled down so In this article, we introduce the Open Semantic that, today, virtually all things can participate in Framework (OSF) as an enabler for this paradigm the WoT. shift. The OSF supports collection, curation, and As things can now readily be interconnected, access to ontologies that encapsulate knowledge the next challenge researchers and practitioners and experience in a machine-understandable way. face is how to make sense of all the connected It thereby forms the basis for enabling automated resources to create intelligent systems.3 Indeed, reasoning and decision making on top of knowl- supporting users in combining services provided edge models and lets semantic applications use do- by smart devices remains a core challenge for main-specifi c and general knowledge models. The ubiquitous computing research.4 Likewise, in OSF furthermore tackles several major obstacles to industrial settings, the easy reconfi guration of the widespread use of integrated semantic models manufacturing environments is gaining impor- by supporting individuals who are not versed with tance, but it depends on production resources ontologies in understanding and extending them, being aware of one another’s capabilities on a se- and by making these models more tangible with the mantic level.5 help of advanced human–interface technologies. Af- Semantic technologies can add meaning to ma- ter a general introduction to the OSF, we demon- chine-to-machine communication by establishing strate its capabilities within the context of a “safety- ontologies of interlinked terms, concepts, relation- by-design” system for industrial manufacturing, in ships, and entities. This could incur a paradigm which the OSF helps ensure that automatically gen- shift across a broad range of industries: for instance, erated production plans comply with work-safety a model of the steps required when manufactur- regulations. 96 1541-1672/17/$33.00 © 2017 IEEE IEEE INTELLIGENT SYSTEMS Published by the IEEE Computer Society The Open Semantic able specific applications to access it is perhaps impossible to fully au- Framework their required information. In this tomate this process, our approach to To foster the integration of semantic article, we discuss several aspects mitigating this problem is to base KP models and the usage of semantic tech- of the OSF, including how knowl- concepts on agreed-upon industrial nologies, we require a common and in- edge is represented, managed, and standards. In these cases, it is suf- tegrated engineering solution that lets acquired; how the OSF moderates ficient to translate standards docu- us acquire, augment, maintain, access, knowledge access via a controlled ments into a machine-understandable interact with, and reason over machine- set of queries; and the visualization language rather than inventing new understandable knowledge, and deploy support it provides to support us- concepts from a clean slate.4 scalable semantic applications to diverse ers in navigating and learning about Once a model has been acquired, it environments. These are the central stored knowledge. must be validated both syntactically tasks of our OSF. and semantically. For syntactic vali- Although semantic-software devel- Knowledge Management and dation, we propose a mechanism akin opers depend on tools for engineer- Engineering in the OSF to unit testing that is tightly integrated ing and running their applications While our OSF’s core ontologies are with the OSF: KPs supply testing que- (such as Protégé and TopBraid Com- available to all of its client applica- ries that are executed (for all KPs) poser), they lack a common solution tions, the OSF is extended with KPs whenever one of the KPs or one of that integrates semantics tools and that encode domain-specific infor- the OSF’s core ontologies is updated. is suitable for supporting generic ap- Semantic validation, on the other plication development, deployment, hand, is usually performed manually and operation.3 Compounding this with the assistance of subject mat- integration problem, no solution yet Once a model has been ter experts. However, because these exists for the acquisition of knowl- experts usually are not versed in the edge held by subject matter experts, acquired, it must be usage of semantic modeling tools, nor have any ontology visualiza- our OSF needs to render the modeled tion, search, and discovery tools ever validated both syntactically information in a way that they can achieved significant traction. There- digest more easily. fore, an integrated framework for and semantically. semantic application development Knowledge Access in the OSF should not only support users dur- The OSF provides access to stored ing the initial engineering phase and mation for usage by specific clients knowledge both in the core ontolo- provide moderated access to knowl- (see Figure 1). KPs thus enable ver- gies and in KPs through a controlled edge models by (authorized) clients, tical interoperability between agents querying interface inside a REST API but it should also provide visualiza- within a domain (for instance, an (marker 1 in Figure 2). This interface is tion and manipulation tools that help electric car and a charging station), based on prefabricated SPARQL query non-ontologists discover the content whereas their integration with the templates inside a KP (marker 2); and relationships between models, core ontologies ensures horizontal thus, KPs not only determine what easily navigate the model space, and interoperability across domains (for knowledge applications can access, augment models—thereby allow- instance, a charging station with a but also exactly how they access it. ing for continued “in-field” mainte- manufacturing robot). In our OSF, The purpose of this mechanism is nance and improvement of semantic KPs are kept distinct from core on- to prevent unwanted modifications applications. tologies so that they can be loaded to the knowledge models and to for- Our proposed solution, the OSF, independently for usage by different bid clients from extracting all knowl- contains an extensible set of core client applications. edge from the OSF; both aspects are ontologies that capture concepts One time-consuming and risk- of paramount importance for the that cut across domains, such as in- prone aspect of semantic modeling is commercial viability of any semantic formation about units and dimen- the inclusion of subject matter exper- framework. sions.4,8 These core ontologies are tise into a KP and the extension of ex- Based on the OSF’s REST API, ac- integrated with domain-specific isting KPs into new areas of expertise cessing knowledge in the core and in knowledge packs (KPs) that en- through model mapping. Although KPs is straightforward: whenever the JANUARY/FEBRUARY 2017 www.computer.org/intelligent 97 OpenADR QUDT (demand (quantities, response) units) FONM Event (mechanics) (events) CIM W3C Time/ (smart grid) PIM (time/ W3C SSN duration) (sensors) KP: smart W3C PROV Activity grid FSGIM (provenance) (activities) (smart grid) WS- Calendar VoAG (dates/ (governance) events) W3C ORG EMIX WXXM (organizations) (smart grid) (weather) BACnet Core (buildings) Haystack (buildings) BRICK (buildings) KP: smart buildings IFC OWL (buildings) Figure 1. In the Open Semantic Framework (OSF), knowledge is structured in domain-specific knowledge packs (KPs) that depend on several core ontologies. Core ontologies contain information about general concepts, such as quantities, units, events, and device types and capabilities. OSF loads a KP, its query templates are class name as a parameter. The OSF re- and their incorporation into the represented as resources that can be sponds to queries in the SPARQL Query model
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