Semantic Analytics and Ontologies - Data Governance: Creating Value from Information Assets - page 94

Objective Graphs and Quantifying Concepts ........................................117
Objective Graphs ..................................................................................117
Quantifying Concepts ..........................................................................117
Example of a Quality Indicator in QI-RS ..........................................117
Related Work .................................................................................................. 120
Conclusions .....................................................................................................121
Acknowledgement ..........................................................................................121
References ........................................................................................................121
INTRODUCTION
This chapter explains semantic analytics in data governance (DG) by
introducing a framework to define quality indicators and to calculate
their values based on medical databases, where quality indicators are
measures of medical service quality, which are represented by numerical
values. Most importantly, we introduce an ontology called Medical
Service Ontology (MSO) as an example of an ontology that plays the
central role in semantic analytics.
Semantic analytics plays an important role in DG. The term semantic
analytics in this chapter refers to a technique used for semantically
analyzing, retrieving, integrating, or managing data resources in several
databases and on the Internet using ontologies. In fact, it is one of DG's
primary roles to manage and utilize the data accumulated by an organiza-
tion and to use that data for the organizational decision making. However,
for this purpose, it is essential to be able to deal with data in an integrated
manner beyond differences in data formats or expressions. Semantic
analytics judges the semantic identity or similarity between data beyond
syntactic differences, making it possible to collectively deal with the same
or similar data from data resources in various formats. Moreover, ontolo-
gies are important as the fundamental tools of current semantic analysis.
We here explain a role of ontology in semantic analytics by natural lan-
guage processing (NLP). NLP is an area of research and application that
explores how computers can be used to understand and manipulate natu-
ral language text or speech to do useful things [Chowdhury, 2003]. NLP
can be regarded as a basic theory of semantic analytics. Knowledge used
in the four stages of analysis in NLP—morphological analysis, syntactic
analysis, semantic analysis, and context analysis—can roughly be divided