108,540 research outputs found
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
Ontology-based Classification and Analysis of non- emergency Smart-city Events
Several challenges are faced by citizens of urban centers while dealing with
day-to-day events, and the absence of a centralised reporting mechanism makes
event-reporting and redressal a daunting task. With the push on information
technology to adapt to the needs of smart-cities and integrate urban civic
services, the use of Open311 architecture presents an interesting solution. In
this paper, we present a novel approach that uses an existing Open311 ontology
to classify and report non-emergency city-events, as well as to guide the
citizen to the points of redressal. The use of linked open data and the
semantic model serves to provide contextual meaning and make vast amounts of
content hyper-connected and easily-searchable. Such a one-size-fits-all model
also ensures reusability and effective visualisation and analysis of data
across several cities. By integrating urban services across various civic
bodies, the proposed approach provides a single endpoint to the citizen, which
is imperative for smooth functioning of smart cities
Experiences of aiding autobiographical memory Using the SenseCam
Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible
Experiences of aiding autobiographical memory using the sensecam
Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible
Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
In the Internet of Things (IoT) domain, various heterogeneous ubiquitous
devices would be able to connect and communicate with each other seamlessly,
irrespective of the domain. Semantic representation of data through detailed
standardized annotation has shown to improve the integration of the
interconnected heterogeneous devices. However, the semantic representation of
these heterogeneous data sources for environmental monitoring systems is not
yet well supported. To achieve the maximum benefits of IoT for drought
forecasting, a dedicated semantic middleware solution is required. This
research proposes a middleware that semantically represents and integrates
heterogeneous data sources with indigenous knowledge based on a unified
ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the
16th International Middleware Conference (Middleware Doct Symposium 2015),
Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US
Utilising semantic technologies for decision support in dementia care
The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems
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