Title

Semi-automatically building a knowledge base of dietary nutritional information for different medical conditions

Date of Publication

2015

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Nathalie Rose Lim-Cheng

Defense Panel Member

Shirley B. Chu
Ralph Vincent J. Regalado

Abstract/Summary

For years, health articles and dietary advice have been provided by dietitians and clinical nutritionists on different online media. This information is widely accessible through the internet. However, self-diagnosis can cause a number of problems, especially for those untrained in the field of health care. Thus, this poses a need for specialized systems that can effectively relay this information to the general public. Numerous ontologies have been built to address this issue. However, the ontologies in human nutrition currently in existence are built with general use and preventive health maintenance in mind. There is currently no ontology that maps the nutritional aspects of medical conditions to different food items. In addition, the task of populating an ontology is very tedious to do by hand.

This research focuses on the design and development of a system that can semi-automatically populate a knowledge base, in the form of an ontology, which associates the necessary nutrients for medical conditions to food items that contain them. This is done by means of information extraction from various health articles available on the internet. One of the key characteristics of an ontology is its reusability. The knowledge base populated by this system is meant to be used by future systems in the field of health informatics. Based on the results of testing, the system is able to extract instances from online articles at an average precision of 0.7804, recall of 0.5149 and f-measure of 0.5149. The relationships between these instances are also mapped and represented via an ontology. An API has been provided to facilitate access to this populated ontology.

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTU021030

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 computer optical disc ; 4 3/4 in.

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