In information systems the division of a domain into relevant concepts and its formal representation is known as ontology . The ThinkHome ontology can be seen as basis for the proposed system. All data has to be stored and provided in a sophisticated way, supplying the system with needed knowledge. For the storage of information it was decided to use the Web Ontology Language (OWL), mainly because of its formal definition and reasoning capabilities. Furthermore, OWL is one major technology of the so-called Semantic Web. This additionally supports the openness of the ThinkHome knowledge representation.
An OWL datastore contains different constructs to create a formal representation of knowledge. The model, which is similar to a database scheme in database design, is constructed by concepts and properties. A concept defines a general idea of a possible item in the defined knowledge base. For the suggested ThinkHome ontology, such concepts are for example WeatherState including all data concerning immediate exterior circumstances or HumanActor describing the group of human system users. In most ontologies constructed from scratch, it is desired to organize the identified concepts in a subsumption hierarchy, which means in a super-class/sub-class connection. Properties are the relations between these concepts and can be differentiated in two kinds: object properties which establish connections between different concepts and datatype properties which connect concepts with values of a specified datatype. The last basic elements which represent the data are individuals. These are distinct from the conceptual model and act as concrete instantiations. For example, in the field of building information this would be a particular wall separating two defined rooms or a specific window type.
In addition to defining simple relations, several logical restrictions can be put on these basic elements as to create more complex dependencies. One example would be an anonymous superclass restriction, which allows membership in a class to be defined through logically combined properties of a set of individuals. OWL, in the majority of the cases, is restricted to some form of logic such as description logics (DL) in order to make it decidable. This means when DL is enforced, a so-called DL-reasoner (e.g. Pellet ) can infer new information from the ontology. As OWL is an open standard, ontology reuse as well as integration into other projects is possible.
The vision of ThinkHome is to create a comprehensive knowledge base which includes all the different concepts needed to realize energy efficient, intelligent control mechanisms. The knowledge base brings together different branches of control information which all can be seen as universe of discourse for the intelligent multi-agent system. The multi-agent society can subsequently query the facts stored in the ontology, thus enabling intelligent decision-making. Figure 1 shows the main branches of the ontology. The division may not be seen as physical separation of knowledge, but merely as logical segmentation of core concepts. This domain division is further grouped into physically independent ontologies. First and foremost the storage of building information is of great importance, as it can support optimized control strategies striving for energy-efficient operation of the smart home. It is not feasible for a user to enter all these values manually due to the huge effort and lack of knowledge. Thus, a semi-automatic approach is favored. Therefore, for the ThinkHome system, the inclusion of data stored in a building information model (BIM) is considered. Apart from concepts relating to the building, also information about users and their preferences has to be considered. Users in this case may be either human users, but also system agents. The reason for this is that the ontology builds the foundation of a multi-agent system in which intelligent actors can take autonomous actions on behalf of human users. For humans, the knowledge base must know different characteristics (e.g. age, gender) and also keep a user profile. In the user profile, different preferences of the users are stored. Predicted future user behavior is represented in the ontology with the help of habit profiles and patterns. Hereby, predicted schedules for different occupancy states are generated (e.g. day, night, weekends, holidays) and consumption peaks can be anticipated. A building process is further a concept containing elementary operations that are used to describe basic actions in the building. It is also very important to consider exterior influences. These weather and climate data can be used to infer the proper action and perform tasks most energy-efficiently. In addition this information can be exploited in order to guarantee user comfort, for example by natural lighting through sunlight. In the energy information branch reside different available energy providers and their trading conditions. This information is especially valuable when envisioning the integration of the ThinkHome system into a smart grid, as the ontology can for example provide the momentarily best option for energy consumption or recovery. Furthermore, it is important to have an idea of the provided building automation services, as well as equipment available in the smart home. This resource information branch includes white goods, brown goods and automation networks hosting lighting, shading as well as heating, ventilation and air conditioning (HVAC) devices. As the automation networks can be of different types, protocols and manufacturers, it is valuable to present an abstracted view on facilities in an ontology. This way, their definition is generalized, which in turn can support the transparent integration and communication across the different networks. In addition, energy producers like solar collectors or a thermal heat pump are stored in this section. Hence, a complete model of the energy consuming and producing landscape available in the building is depicted in the knowledge base.
The following publications developed in the ThinkHome project are related to knowledge bases and ontologies.
- Mario J. Kofler and Wolfgang Kastner. Towards an ontology representing building physics parameters for increased energy efficiency in smart home operation. In Proceedings of the 2nd Central European Symposium on Building Physic (CESBP2013), Vienna, Austria, 2013. pages 51 -58. [ bib ]
- Wolfgang Kastner, Mario J. Kofler and Christian Reinisch. Wissensrepräsentation für das adaptive Eigenheim im Kontext von Smart Cities.Elektrotechnik und Informationstechnik, 129(4):286-292, 2012. [ bib ]
- Mario J. Kofler and Christian Reinisch and Wolfgang Kastner. An Ontological Weather Representation for Improving Energy-Efficiency in Interconnected Smart Home Systems. In Proceedings of Applied Simulation and Modelling/Artificial Intelligence and Soft Computing (ASC2012), Napoli, Italy, 2012. pages 256-263. [ bib ]
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: E. Sirin, B. Parsia, B. Grau, A. Kalyanpur, and Y. Katz, “Pellet: A practical OWL-DL reasoner,” Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5, no. 2, pp. 51–53, 2007.