W ith the Semantic Web relying on ontologies to establish online machineinterpretable information, the Internet is growing into a semantically aware computing paradigm that facilitates Web entities' discovery of the knowledge and resources they need. Ambient intelligence aims to enable smart interaction beyond the Internet by embedding intelligence into our environment to unobtrusively support users' daily activities. To accomplish these goals, ontologies and semantic awareness are crucial for better understanding a user's context. While interest in the Semantic Web has spurred the development of large-scale semantic grid architectures, expanding the Semantic Web to the other side of the computing spectrum is a complex undertaking. The techniques and tools that support the Semantic Web aren't designed to deal with the resource-constrained devices with which people frequently interact in an ambient-intelligence environment. To counter this disadvantage, we developed a coding scheme for ontologies that embeds semantic awareness in devices with limited memory and processing capabilities, such as sensory nodes and smart phones. This scheme provides a compact representation of an ontology and is enhanced with an efficient and effective semantic-matching algorithm. Ambient intelligence envisions computing devices pervasively and unobtrusively embedded in the environment, connecting hardware and software infrastructures, collecting information on users and their context, and adapting these devices' behavior to better assist the users. Ontologies help structure this information by using an established vocabulary of terms and concepts in a specific domain of interest. You can use them to describe the semantics of services offered in the environment, a device's capabilities, and a user's preferences and activities. In general, they characterize the situation of entities (person, place, or object) considered relevant to the interaction between a user and an application. 1 The OWL specification provides an expressive language with many logical constructs to define classes and properties and their relationships. Ontologies can reuse classes and properties defined in another ontology without having to remodel all the relationships that hold among them. The reuse of classes and properties by linking to other machineinterpretable ontologies facilitates the semantic matching and uniform interpretation of any shared information. Semantic matching is often accomplished through subsumption queries. Subsumption refers to the reflexive, transitive, and antisymmetric relationship between classes that states that a class A subsumes a class B if and only if the set of instances of class A includes the set of instances of
In particular, in REF a coding scheme for ontologies that embeds semantic awareness into devices with limited memory and processing capabilities is developed.
Encoding Semantic Awareness in Resource-Constrained Devices
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science