Abstract
Ontologies have established themselves as the single most important semantic web technology. They have attracted widespread interest from both academic and industrial domains. This has led to an increase in ontologies created. It has become apparent that more than one ontology may model the same domain yet they can be very different. The question then is, how do you determine which ontology best fits your purposes? This paper endeavours to answer this question by reviewing relevant literature and instantiating the data-driven ontology evaluation methodology in the context of workflow ontologies. This evaluation methodology is then evaluated through statistical means particularly the Kruskal-Wallis test and further post hoc testing using the Mann-Whiteny U test.
| Original language | English |
|---|---|
| Title of host publication | IC3K 2013; KEOD 2013 - 5th International Conference on Knowledge Engineering and Ontology Development, Proceedings |
| Pages | 207-213 |
| Number of pages | 7 |
| Publication status | Published - Nov 21 2013 |
| Event | 5th International Conference on Knowledge Engineering and Ontology Development, KEOD 2013 - Vilamoura, Algarve, Portugal Duration: Sep 19 2013 → Sep 22 2013 |
Other
| Other | 5th International Conference on Knowledge Engineering and Ontology Development, KEOD 2013 |
|---|---|
| Country | Portugal |
| City | Vilamoura, Algarve |
| Period | 9/19/13 → 9/22/13 |
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All Science Journal Classification (ASJC) codes
- Software
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Contributing evidence to data-driven ontology evaluation workflow ontologies perspective. / Hlomani, Hlomani; Stacey, Deborah A.
IC3K 2013; KEOD 2013 - 5th International Conference on Knowledge Engineering and Ontology Development, Proceedings. 2013. p. 207-213.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Contributing evidence to data-driven ontology evaluation workflow ontologies perspective
AU - Hlomani, Hlomani
AU - Stacey, Deborah A.
PY - 2013/11/21
Y1 - 2013/11/21
N2 - Ontologies have established themselves as the single most important semantic web technology. They have attracted widespread interest from both academic and industrial domains. This has led to an increase in ontologies created. It has become apparent that more than one ontology may model the same domain yet they can be very different. The question then is, how do you determine which ontology best fits your purposes? This paper endeavours to answer this question by reviewing relevant literature and instantiating the data-driven ontology evaluation methodology in the context of workflow ontologies. This evaluation methodology is then evaluated through statistical means particularly the Kruskal-Wallis test and further post hoc testing using the Mann-Whiteny U test.
AB - Ontologies have established themselves as the single most important semantic web technology. They have attracted widespread interest from both academic and industrial domains. This has led to an increase in ontologies created. It has become apparent that more than one ontology may model the same domain yet they can be very different. The question then is, how do you determine which ontology best fits your purposes? This paper endeavours to answer this question by reviewing relevant literature and instantiating the data-driven ontology evaluation methodology in the context of workflow ontologies. This evaluation methodology is then evaluated through statistical means particularly the Kruskal-Wallis test and further post hoc testing using the Mann-Whiteny U test.
UR - http://www.scopus.com/inward/record.url?scp=84887770588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887770588&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9789898565815
SP - 207
EP - 213
BT - IC3K 2013; KEOD 2013 - 5th International Conference on Knowledge Engineering and Ontology Development, Proceedings
ER -