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

GLYDE - An Expressive XML Standard for the Representation of Glycan

The amount of glycomics data being generated is rapidly increasing as a result of improvements in analytical and computational methods. Correlation and analysis of this large, distributed data set requires an extensible and flexible representational standard that is also ‘understood’ by a wide range of software applications. An XML-based data representation standard that faithfully captures essential structural details of a glycan moiety along with additional information (such as data provenance) to aid the interpretation and usage of glycan data, will facilitate the exchange of glycomics data across the scientific community. To meet this need, we introduce GLYcan Data Exchange (GLYDE) standard as an XML-based representation format to enable interoperability and exchange of glycomics data. An online tool (http://128.192.9.86/stargate/formatIndex.jsp) for the conversion of other representations to GLYDE format has been developed

W3C Semantic Sensor Networks: Ontologies, Applications, and Future Directions

Plenary Talk discussing the W3C Semantic Sensor Network, including the ontology, applications, and future directions

A Semantics-Based Approach to Machine Perception

Machine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices.Advances in sensing technology hold the promise to revolutionize our ability to observe and understand the world around us. Yet the gap between observation and understanding is vast. As sensors are becoming more advanced and cost-effective, the result is an avalanche of data of high volume, velocity, and of varied type, leading to the problem of too much data and not enough knowledge (i.e., insights leading to actions). Current estimates predict over 50 billion sensors connected to the Web by 2020. While the challenge of data deluge is formidable, a resolution has profound implications. The ability to translate low-level data into high-level abstractions closer to human understanding and decision-making has the potential to disrupt data-driven interdisciplinary sciences, such as environmental science, healthcare, and bioinformatics, as well as enable other emerging technologies, such as the Internet of Things.The ability to make sense of sensory input is called perception; and while people are able to perceive their environment almost instantaneously, and seemingly without effort, machines continue to struggle with the task. Machine perception is a hard problem in computer science, with many fundamental issues that are yet to be adequately addressed, including: (a) annotation of sensor data, (b) interpretation of sensor data, and (c) efficient implementation and execution. This dissertation presents a semantics-based machine perception framework to address these issues.The tangible primary contributions created to support the thesis of this dissertation include the development of a Semantic Sensor Observation Service (SemSOS) for accessing and querying sensor data on the Web, an ontology of perception (Intellego) that provides a formal semantics of machine perception and reasoning framework for the interpretation of sensor data, and efficient algorithms for the machine perception inference tasks to enable interpretation of sensor data on resource-constrained devices, such as smart phones. Each of these contributions has been prototyped, evaluated, and applied towards solving real-world problems in multiple domains including weather and healthcare