TITLE:
Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery
AUTHORS:
Feichen Shen, Hongfang Liu, Sunghwan Sohn, David W. Larson, Yugyung Lee
KEYWORDS:
Biomedical Knowledge Discovery, Pattern Analysis, Predicate, Query Generation
JOURNAL NAME:
Intelligent Information Management,
Vol.8 No.3,
May
31,
2016
ABSTRACT: In the current biomedical data movement,
numerous efforts have been made to convert and normalize a large number of
traditional structured and unstructured data (e.g., EHRs, reports) to
semi-structured data (e.g., RDF, OWL). With the increasing number of
semi-structured data coming into the biomedical community, data integration and
knowledge discovery from heterogeneous domains become important research
problem. In the application level, detection of related concepts among medical
ontologies is an important goal of life science research. It is more crucial to
figure out how different concepts are related within a single ontology or
across multiple ontologies by analysing predicates in different knowledge
bases. However, the world today is one of information explosion, and it is
extremely difficult for biomedical researchers to find existing or potential
predicates to perform linking among cross domain concepts without any support
from schema pattern analysis. Therefore, there is a need for a mechanism to do
predicate oriented pattern analysis to partition heterogeneous ontologies into
closer small topics and do query generation to discover cross domain knowledge
from each topic. In this paper, we present such a model that predicates
oriented pattern analysis based on their close relationship and generates a
similarity matrix. Based on this similarity matrix, we apply an innovated
unsupervised learning algorithm to partition large data sets into smaller and
closer topics and generate meaningful queries to fully discover knowledge over
a set of interlinked data sources. We have implemented a prototype system named
BmQGen and evaluate the proposed model with colorectal surgical cohort from the
Mayo Clinic.