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Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction
Published on 2017-11-28880 Views
Automated acquisition (learning) of ontologies from data has attracted research interest because it can complement manual, expensive construction of ontologies. We investigate the problem of General T
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Presentation
Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction00:00
OWL Ontology00:14
Traditional Ontology Engineering01:19
Why Not Learn From Data?01:49
Ontology learning02:29
Challenges in Ontology Learning02:36
Where Are Good Hypotheses?03:35
What are C, D in C vD? Let’s Ask Data!04:50
Theoretical Guarantees of DL-Apriori07:08
How to Measure Quality of Hypotheses?07:32
Statistical Quality Measures08:00
Logical quality measures10:12
Putting All The Pieces Together: DL-MINER10:36
Time to Evaluate11:27
Mutual Correlations of Quality Measures11:44
Examples of Mined Hypotheses12:22
Case Study: Human Feedback (1)12:51
Case Study: Human Feedback (2)13:43
DL-Miner in Protégé14:12
Future Work14:27
Your Take-Away Message14:48
Questions15:00