Payam M. Barnaghi

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The W3C Semantic Sensor Network Incubator group (the SSN-XG), as one of its activities, produced an OWL 2 ontology to describe sensors and observations the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe the capabilities of sensors, the measurement processes used and the resultant observations, and can be aligned(More)
ions should be generated efficiently – Sensors are constantly streaming observation data in real-time. Therefore, to be practically useful, the generation of abstractions should also be computed in near-real-time. In addition, in many applications, the perception process must compute abstractions of sensor observations within resource-constrained(More)
Probabilistic topic models were originally developed and utilized for document modeling and topic extraction in Information Retrieval. In this paper, we describe a new approach for automatic learning of terminological ontologies from text corpus based on such models. In our approach, topic models are used as efficient dimension reduction techniques, which(More)
The Internet of Things envisions a multitude of heterogeneous objects and interactions with the physical environment. The functionalities provided by these objects can be termed as ‘real-world services’ as they provide a near real-time state of the physical world. A structured, machine-processible approach to provision such real-world services(More)
Developments in (wireless) sensor and actuator networks and the capabilities to manufacture low cost and energy efficient networked embedded devices have lead to considerable interest in adding real world sense to the Internet and the Web. Recent work has raised the idea towards combining the Internet of Things (i.e. real world resources) with semantic Web(More)
This paper describes a linked-data platform to publish sensor data and link them to existing resource on the semantic Web. The linked sensor data platform, called Sense2Web supports flexible and interoperable descriptions and provide association of different sensor data ontologies to resources described on the semantic Web and the Web of data. The current(More)
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology-enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services, such as traffic, public(More)
In this paper we investigate the use of probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors provide a model to represent service descriptions of any type in vector form. With this conversion, heterogeneous service descriptions can be represented on the same homogeneous plane(More)