Sensor Data and Perception: Can Sensors Play 20 Questions

Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data, yet we are able to make sense of our environment with relative ease. Why is the task of perception so easy for us, and so hard for machines; and could this have anything to do with how we play the game 20 Questions

An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices

The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale

Analysis on Partial Relationship in LOD

Relationships play a key role in Semantic Web to connect the dots between entities (concepts or instances) in a way that enables to absorb the real sense of the entities. Some interesting relationships would give proof for the existence of subject and object in triples which in tern can be defined as evidential relationships. Identifying evidential relationships will yield solutions to some existing inference problems and open doors for new applications and research. Part_of relationships are identified as a special kind of an evidential relationship out of membership, causality and etc. Linked Open data as a global data space would provide a good platform to explore these relationships and solve interesting inference problems. But this is not trivial because LOD does not have a rich schema in terms of the data sets and also the existing work with respect to schema mapping in LOD is limited to concepts and not relationships. This project is based on finding a novel approach to identify partial relationships which is the superset of part_of relationships from LOD instance data by conducting a proper analysis of the data patterns in instance data. Ultimately this approach would provide a way to enhance the shallow schemas in LOD which in tern would be helpful in schema matching in LOD. We apply the determined approach to the DBpedia data set in order to identify the partial relationships in DBpedia

Trust Model for Semantic Sensor and Social Networks: A Preliminary Report

Trust is an amorphous concept that is becoming Increasingly important in many domains, such as P2P networks, E-commerce, social networks, and sensor networks. While we all have an intuitive notion of trust, the literature is scattered with a wide assortment of differing definitions and descriptions; often these descriptions are highly dependent on a single domain or application of interest. In addition, they often discuss orthogonal aspects of trust while continuing to use the general term “trust”. In order to make sense of the situation, we have developed an ontology of trust that integrates and relates its various aspects into a single model