,
Ontology-Driven Information Extraction and Knowledge Acquisition from Heterogeneous, Distributed, Autonomous Biological Data Sources
(2001)
Аннотация
Scientific discovery in data rich domains (e.g., biological sciences,
atmospheric sciences) presents several challenges in information
extraction and knowledge acquisition from heterogeneous, distributed,
autonomously operated, dynamic data sources. This paper
describes these problems and outlines the key elements of algorithmic
and systems solutions for computer assisted scientific discovery
in
such domains. These include: ontology-assisted approaches to customizable
data integration and information extraction from
heterogeneous, distributed data sources; distributed data mining algorithms
for knowledge acquisition from large, distributed data sets
which obviate the need for transmitting large volumes of data across
the network; ontology-driven approaches to exploratory data
analysis from alternative ontological perspectives; and modular and
extensible agent-based implementations of the algorithms within a
platform-independent agent infrastructure. Prototype implementations
of the proposed system are being used for discovery of
macromolecular structure-function relationships in computational biology
and distributed coordinated intrusion detection in computer
networks.
