Abstract

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.

Highlights

  • There is an increasing demand for sharing and integration of medical data in biomedical research

  • We have built a SPARQL query endpoint on a single machine that is hosted at the UMKC Distributed Intelligent Computing (UDIC) lab

  • We presented the MedKDD framework for knowledge discovery and semantic interoperability through the discovery of the Cross Domain Neighborhood Patterns (CDNP) from the heterogeneous information network of the multiple medical ontologies

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Summary

Introduction

There is an increasing demand for sharing and integration of medical data in biomedical research. Heterogeneous information networking on the cloud are designed to enable compliant sharing of data based on the relationships across domains [1]. The Linked Open Data project is a notable effort for creating a knowledge space of RDF documents linked together and sharing a common ontology [2]. RDF is a metadata data model designed by the World Wide Web for conceptual modeling of information on the Web [3]. SPARQL Protocol and RDF Query Language is an RDF query language for semantic query language to retrieve data stored in RDF format [4]. According to the Linked Open Data project, the Web of Data currently

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