Authors:
Samia Sbissi
1
;
Mariem Mahfoudh
2
and
Said Gattoufi
1
Affiliations:
1
SMART Laboratory, Tunis University, Tunis and Tunisia
;
2
MIRACL Laboratory, University of Sfax, Sfax, Tunisia, ISIGK, University of Kairouan, Kairouan and Tunisia
Keyword(s):
Ontology Learning, Ontology Enrichment, SWRL, Word2Vec.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Natural Language Processing
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Ontology Sharing and Reuse
;
Pattern Recognition
;
Symbolic Systems
Abstract:
In order to assist professionals and doctors to make decisions about appropriate health care for patients who are at risk of cardiovascular disease, we propose a decision support system based on OWL (Ontology Language Web) ontology with SWRL (semantic web rule language) rules. The idea consists to parse clinical practice guidelines (i.e. documents that contain recommendations and medical knowledges) to enrich and exploit existing cardiovascular domain ontology. The enrichment process is conducted by ontology learning task. We first pre-process the text and extract the relevant concepts. Then, we enrich the ontology not only by OWL DL axioms, but also SWRL rules. To identify the similarity between terms texts and ontology concepts, we have used a combination of methods as levenshtein similarity and Word2Vec.