Semantic applications can help commercial applications perform quickly and reliably by improving ecosystem interoperability. Converting and integrating current standards specifications to OWL models could support the adoption of semantic models, as well as machine-processable standards compliance and data interoperability.

Complexity of algorithms and policies in energy systems has increased interactions among heterogeneous systems and information sources. Demand Response (DR) is a representative example where electrical grids and buildings communicate with each other to manage overall electricity demands. There are various industry standards that model the information and the interactions between grids and buildings. However, each building has all the burden of converting information from different models to its information model prior to using it. We envision a system-agnostic information model for these interactions that can reduce information conversion to/from buildings and invigorate the interaction. We propose an integration of ontologies and standards to express DR interactions and open the possibility of connecting other information models. We also suggest an information mediator system that interprets and delivers messages among grids and buildings and can mediate energy usage among buildings based on the system-agnostic model.

This paper describes our work concerning the definition of a neutral, abstract ontology, and framework that supports the vision and diverse contexts of a smart community. This framework is composed of a general, core ontology that supports what many are calling the Internet of Things (IoT), a scalable number of extension ontologies to describe various application perspectives, and a mapping methodology to relate external data and/or schemas to our ontology. Finally, we show why this ontology is scalable and generic enough to support a wide range of smart devices, systems, and people.

The goal of the Ontology Summit 2013 was to create guidance for ontology developers and users on how to evaluate ontologies. Over a period of four months a variety of approaches were discussed by participants, who represented a broad spectrum of ontology, software, and system developers and users. We explored how established best practices in systems engineering and in software engineering can be utilized in ontology development.

ProblemCurrently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated.Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies.This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology. ApproachThe goal of the Ontology Summit 2013 was to create guidance for ontology developers and users on how to evaluate ontologies.Over a period of four months a variety of approaches were discussed by participants, who represented a broad spectrum of ontology, software, and system developers and users.We explored how established best practices in systems engineering and in software engineering can be utilized in ontology development. ResultsThis document focuses on the evaluation of five aspects of the quality of ontologies: intelligibility, fidelity, craftsmanship, fitness, and deployability.A model for the ontology life cycle is presented, and

In an effort to reduce healthcare costs, home healthcare devices are seeing increased use. However, many of these devices are specialized, operate independently and use proprietary interfaces, diluting the cost savings. Smartphones offer a way to retain these cost savings by serving as a sensor hub within the home. In this paper we describe an extensible framework that combines a mobile device interfacing with health sensors in the home, a set of ontologies to map sensor data to clinical information standards such as IEEE 11073, and a cloud-based reasoning and mapping system built with OWL, SPARQL and SPIN. This approach capitalizes on the ease of use and low cost of smartphones, and the computational power and flexibility of a cloud-based semantic web reasoner. A proof of concept implementation is presented.

The Ontology Summit 2012 explored the current and potential uses of ontology, its methods and paradigms, in big systems and big data: How ontology can be used to design, develop, and operate such systems. The systems addressed were not Just software systems, although software systems are typically core and necessary components, but more complex systems that include multiple kinds and levels of human and community interaction with physical-software systems, systems of systems, and the socio-technical environments for those systems which can include cultural, legal, and economic components. The focus themes used for this exploration were Big Systems Engineering, Big Data Challenge, Large Scale Domain Applications, and cross-cutting aspects Ontology Quality, and Federation and Integration of Systems.The Ontology Summit 2012 consisted of over three months of intensive virtual collaborative elaboration of these issues in presentations, panels, and group email. The culmination of these activities was a face-to-face Symposium at the US National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA, 12-13 April 2012. The primary product of this Ontology Summit is the communique reported here. But there are other products, some continuing as collaborative, more specifically focused analysis and modeling efforts aligned with various open standards activities.Behind all of these particular products, of course, is the real overriding purpose of the Ontology Summit 2012, which was: the joint collaboration of three distinct communities, the ontology, systems engineering and big systems stakeholder communities, who came together to address common problems, create common understanding and propose common solutions.

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance (in terms of accuracy, domain coverage and the efficiency and quality of automated reasoning using the ontologies) on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.

The high cost of healthcare in America is well-known, with imperfect interoperability adding as much as $77.8 billion/year to that cost, according to one study [1]. One of the principal causes of this high cost is the large amount of "paperwork" - the administrative expenses incurred for each medical procedure. While the debate continues about whether the country would be better off with a single-payer medical system or the current private insurance approach, there are a number of ways to reduce the administrative costs without excessive risk. These costs are essentially about getting information in the right form, to the right place, at the right time - a challenge that is by no means unique to the healthcare industry. This presentation identifies some of the approaches that have been successful in the manufacturing sector to make the sharing of information - interoperability - more efficient. Manufacturing technology has been developing and improving for decades. Principles like lean manufacturing, paperless design, ISO 9000 performance practices and Taguchi methods are a given for today's major manufacturers. With trends toward global manufacturing and outsourcing, practices have now evolved to Internet-based communication of engineering designs, inventory levels, purchase orders, and a wide variety of other logistical, financial and technical data. Supporting all this, a suite of standards has been developed, deployed, and winnowed down to some that really help, leaving others that seemed promising but failed to meet expectations by the wayside.

Designers and engineers use various engineering authoring tools, such as CAD, CAE, and PDM, to generate information objects (engineering objects). On the business side, enterprise level business process modelers use various business authoring tools, such as ERP, CRM, and LCA, to generate information objects (business objects). These information objects, both engineering and business, are represented using information standards. These standards are used to exchange information about engineering and business systems for enterprise level interoperability. One of the main problems of designers, engineers and process modelers is the selection of appropriate standards for interoperability. To ensure enterprise level interoperability, it is absolutely critical that information standards are compared and harmonized as there are overlapping and dissimilar standards available. In this paper, we will sketch a method towards comparing and harmonizing standards based on: 1) informal approach, 2) typology of standards, 3) use-case scenarios, and 4) ontologies. The method is explained using some engineering and business standards.

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance and accuracy on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.

As manufacturing and commerce become ever more global, companies are dependent increasingly upon the efficient and effective sharing of information with their partners, wherever they may be. Leading manufacturers perform this sharing with computers, which must therefore have the required software to encode and decode the associated electronic transmissions. Because no single company can dictate that all its partners use the same software, standards for how the information is represented become critical for error-free transmission and translation. The terms interoperability and integration are frequently used to refer to this error-free transmission and translation. This paper summarizes two projects underway at the National Institute of Standards and Technology in the areas of interoperability testing and integration automation. These projects lay the foundation for at tomorrow’s standards, which we believe will rely heavily upon the use of formal logic representations, commonly called ontologies.