8,938 research outputs found
Linked Data based video annotation and browsing for distance learning
We present a pair of prototype tools that enable users to mark up video with annotations and later explore related materials using Semantic Web and Linked Data approaches. The �first tool helps academics preparing Open University course materials to mark up videos with information about the subject matter and audio-visual content. The second tool enables users, such as students or academics, to find video and other materials relevant to their study
Semantic Gateway as a Service architecture for IoT Interoperability
The Internet of Things (IoT) is set to occupy a substantial component of
future Internet. The IoT connects sensors and devices that record physical
observations to applications and services of the Internet. As a successor to
technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has
stumbled into vertical silos of proprietary systems, providing little or no
interoperability with similar systems. As the IoT represents future state of
the Internet, an intelligent and scalable architecture is required to provide
connectivity between these silos, enabling discovery of physical sensors and
interpretation of messages between things. This paper proposes a gateway and
Semantic Web enabled IoT architecture to provide interoperability between
systems using established communication and data standards. The Semantic
Gateway as Service (SGS) allows translation between messaging protocols such as
XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of
broadly accepted specifications such as W3C's Semantic Sensor Network (SSN)
ontology for semantic annotations of sensor data provide semantic
interoperability between messages and support semantic reasoning to obtain
higher-level actionable knowledge from low-level sensor data.Comment: 16 page
Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology
Wiljes C, Cimiano P. Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology. In: Proceedings of the Workshop on the Semantic Publishing (SePublica 2012). 2012: 48-59.The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist
TechMiner: Extracting Technologies from Academic Publications
In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision
A Data Annotation Architecture for Semantic Applications in Virtualized Wireless Sensor Networks
Wireless Sensor Networks (WSNs) have become very popular and are being used
in many application domains (e.g. smart cities, security, gaming and
agriculture). Virtualized WSNs allow the same WSN to be shared by multiple
applications. Semantic applications are situation-aware and can potentially
play a critical role in virtualized WSNs. However, provisioning them in such
settings remains a challenge. The key reason is that semantic applications
provisioning mandates data annotation. Unfortunately it is no easy task to
annotate data collected in virtualized WSNs. This paper proposes a data
annotation architecture for semantic applications in virtualized heterogeneous
WSNs. The architecture uses overlays as the cornerstone, and we have built a
prototype in the cloud environment using Google App Engine. The early
performance measurements are also presented.Comment: This paper has been accepted for presentation in main technical
session of 14th IFIP/IEEE Symposium on Integrated Network and Service
Management (IM 2015) to be held on 11-15 May, 2015, Ottawa, Canad
Horizontal Integration of Warfighter Intelligence Data: A Shared Semantic Resource for the Intelligence Community
We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of intelligence data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts
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