329 research outputs found
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data
We compare two distinct approaches for querying data in the context of the
life sciences. The first approach utilizes conventional databases to store the
data and intuitive form-based interfaces to facilitate easy querying of the
data. These interfaces could be seen as implementing a set of "pre-canned"
queries commonly used by the life science researchers that we study. The second
approach is based on semantic Web technologies and is knowledge (model) driven.
It utilizes a large OWL ontology and same datasets as before but associated as
RDF instances of the ontology concepts. An intuitive interface is provided that
allows the formulation of RDF triples-based queries. Both these approaches are
being used in parallel by a team of cell biologists in their daily research
activities, with the objective of gradually replacing the conventional approach
with the knowledge-driven one. This provides us with a valuable opportunity to
compare and qualitatively evaluate the two approaches. We describe several
benefits of the knowledge-driven approach in comparison to the traditional way
of accessing data, and highlight a few limitations as well. We believe that our
analysis not only explicitly highlights the specific benefits and limitations
of semantic Web technologies in our context but also contributes toward
effective ways of translating a question in a researcher's mind into precise
computational queries with the intent of obtaining effective answers from the
data. While researchers often assume the benefits of semantic Web technologies,
we explicitly illustrate these in practice
Semantically Resolving Type Mismatches in Scientific Workflows
Scientists are increasingly utilizing Grids to manage large data sets and execute scientific experiments on distributed resources. Scientific workflows are used as means for modeling and enacting scientific experiments. Windows Workflow Foundation (WF) is a major component of Microsoft’s .NET technology which offers lightweight support for long-running workflows. It provides a comfortable graphical and programmatic environment for the development of extended BPEL-style workflows. WF’s visual features ease the syntactic composition of Web services into scientific workflows but do nothing to assure that information passed between services has consistent semantic types or representations or that deviant flows, errors and compensations are handled meaningfully. In this paper we introduce SAWSDL-compliant annotations for WF and use them with a semantic reasoner to guarantee semantic type correctness in scientific workflows. Examples from bioinformatics are presented
Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments
The file attached to this record is the author's final peer reviewed versionActivity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored
in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific
preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively
Ontologies on the semantic web
As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The “Semantic Web” was touted by its developers as equally revolutionary but has not yet achieved anything like the Web’s exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT
A semantics-based approach to sensor data segmentation in real-time Activity Recognition
Department of Information Engineering, Dalian University, China
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Activity Recognition (AR) is key in context-aware assistive living systems. One
challenge in AR is the segmentation of observed sensor events when interleaved
or concurrent activities of daily living (ADLs) are performed. Several studies
have proposed methods of separating and organising sensor observations and
recognise generic ADLs performed in a simple or composite manner. However,
little has been explored in semantically distinguishing individual sensor events
directly and passing it to the relevant ongoing/new atomic activities. This
paper proposes Semiotic theory inspired ontological model, capturing generic
knowledge and inhabitant-specific preferences for conducting ADLs to support
the segmentation process. A multithreaded decision algorithm and system prototype
were developed and evaluated against 30 use case scenarios where each
event was simulated at 10sec interval on a machine with i7 2.60GHz CPU, 2
cores and 8GB RAM. The result suggests that all sensor events were adequately
segmented with 100% accuracy for single ADL scenarios and minor improvement
of 97.8% accuracy for composite ADL scenario. However, the performance has
suffered to segment each event with the average classification time of 3971ms
and 62183ms for single and composite ADL scenarios, respectively
Computational Ontologies and Information Systems II: Formal Specification
This paper extends the study of ontologies in Part I of this study (Volume 14, Article 8) in the context of Information Systems. The basic foundations of computational ontologies presented in Part I are extended to formal specifications in this paper. This paper provides a review of the formalisms, languages, and tools for specifying and implementing computational ontologies Directions for future research are also provided
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