2,399 research outputs found
Semantic processing of EHR data for clinical research
There is a growing need to semantically process and integrate clinical data
from different sources for clinical research. This paper presents an approach
to integrate EHRs from heterogeneous resources and generate integrated data in
different data formats or semantics to support various clinical research
applications. The proposed approach builds semantic data virtualization layers
on top of data sources, which generate data in the requested semantics or
formats on demand. This approach avoids upfront dumping to and synchronizing of
the data with various representations. Data from different EHR systems are
first mapped to RDF data with source semantics, and then converted to
representations with harmonized domain semantics where domain ontologies and
terminologies are used to improve reusability. It is also possible to further
convert data to application semantics and store the converted results in
clinical research databases, e.g. i2b2, OMOP, to support different clinical
research settings. Semantic conversions between different representations are
explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which
can also generate proofs of the conversion processes. The solution presented in
this paper has been applied to real-world applications that process large scale
EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015,
preprint versio
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
Data semantic enrichment for complex event processing over IoT Data Streams
This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation
Semiotics:Semantic model-driven development for IoT interoperability of emergency services
Modern early warning systems (EWSs) use Internet-of-Things (IoT) technologies to realize real-time data acquisition, risk detection and message brokering between data sources and warnings' destinations. Interoperability is crucial for effective EWSs, enabling the integration of components and the interworking with other EWSs. IoT technologies potentially improve the EWS efficiency and effectiveness, but this potential can only be exploited if interoperability challenges are properly addressed. The three main challenges for interoperability are: (1) achieving semantic integration of a variety of data sources and different representations; (2) supporting time- and safety-critical applications with performance and scalability; and (3) providing data analysis for effective responses with personalized information requirements. In this paper, we describe the “SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS” (SEMIoTICS) framework, which supports the development of semantic interoperable IoT EWSs. The framework has been validated with a pilot performed with accident use cases at the port of Valencia. The validation results show that it fulfils the requirements that we derived from the challenges above.</p
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