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Mining Hypotheses from Data in OWL: Advanced Evaluation and Complete Construction
Published on Nov 28, 2017868 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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Chapter list
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