TITLE:
BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data
AUTHORS:
Feichen Shen, Yugyung Lee
KEYWORDS:
Knowledge Discovery, Ontology, Linked Data
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.10 No.1,
March
21,
2018
ABSTRACT:
A large number of ontologies have been introduced by the biomedical community
in recent years. Knowledge discovery for entity identification from
ontology has become an important research area, and it is always interesting
to discovery how associations are established to connect concepts in a single
ontology or across multiple ontologies. However, due to the exponential
growth of biomedical big data and their complicated associations, it becomes
very challenging to detect key associations among entities in an inefficient
dynamic manner. Therefore, there exists a gap between the increasing needs
for association detection and large volume of biomedical ontologies. In this
paper, to bridge this gap, we presented a knowledge discovery framework, the
BioBroker, for grouping entities to facilitate the process of biomedical knowledge
discovery in an intelligent way. Specifically, we developed an innovative
knowledge discovery algorithm that combines a graph clustering method and
an indexing technique to discovery knowledge patterns over a set of interlinked
data sources in an efficient way. We have demonstrated capabilities of
the BioBroker for query execution with a use case study on a subset of the
Bio2RDF life science linked data.