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Semantic Big Data in Australia - from Dingoes to Drysdale
Published on Nov 28, 20134362 Views
This keynote will describe a number of projects being undertaken at the University of Queensland eResearch Lab that are pushing Semantic Web technologies to their limit to help solve grand challenges
Related categories
Chapter list
Semantic Big Data in Australia – from Dingoes to Drysdale00:00
Overview00:05
Projects00:37
Semantic Annotation of Animal Accelerometry Data01:26
User Driven Requirements02:56
Objectives03:46
Methodology04:09
Architecture04:50
User Interface - Tagging05:26
User Interface – Automated Results05:49
Evaluation06:09
Results06:32
Benefits07:10
OzTrack Background07:56
Objectives08:33
OzTrack Interface09:51
Define New Projects09:59
Data cleansing - 110:42
Data cleansing - 211:14
View Specific Animal Track11:20
View Raw Data11:28
Calculate Home Ranges - 111:34
Calculate Home Ranges - 211:47
Calculate Home Ranges - 311:48
Environmental layers13:16
OzTrack13:52
Semantic Sensor Networks14:37
Picture15:03
Data Quality – SOUE Detector15:29
Example: Negative Correlation16:32
Exploit Expert-defined Correlations17:01
SSN Ontology17:20
SSN Ontology + Extensions17:30
Correlated Environmental Sensor Properties (CESP) ontology - 117:44
Correlated Environmental Sensor Properties (CESP) ontology - 218:04
Correlated Environmental Sensor Properties (CESP) ontology - 318:48
Semantic Fire Weather Index19:06
System Architecture19:56
Ontology Extensions: FWI, Prov - 120:27
Ontology Extensions: FWI, Prov - 220:45
Comparison with BoM FWIs21:21
NSW Bushfire Command Centre21:55
Skeletome - 122:49
Example24:03
Challenges24:43
Community Needs25:04
The Platform25:24
Bone Dysplasia Ontology26:02
Knowledge Base of Disorders - 126:29
Knowledge Base of Disorders - 226:42
Knowledge Base of Disorders - 326:48
Knowledge Base of Disorders - 427:04
Knowledge Base of Disorders - 527:09
Knowledge Base of Disorders - 627:14
Knowledge Base of Disorders - 727:19
Skeletome - 227:31
Patient Sharing28:10
Discussing a Patient - 128:26
Discussing a Patient - 228:27
Discussing a Patient - 328:38
Discussing a Patient - 428:46
Discussing a Patient - 528:51
Entity Term Extraction - 128:55
Entity Term Extraction - 228:57
Entity Term Extraction - 329:02
Entity Term Extraction - 429:32
Entity Term Extraction - 529:36
Reasoning29:47
The Twentieth Century in Paint32:05
User-driven Requirements - 132:47
User-driven Requirements - 234:40
Aims of 20th Century Paint35:24
Heterogeneous Entities36:03
Objectives36:13
Case Study37:59
CIDOC-CRM – top level38:38
OPPRA Ontologies - 139:15
OPPRA Ontologies - 239:35
OPPRA - Material40:07
Modelling of Experiments40:16
Modelling Publication Data41:13
Web Portal - Architecture42:37
Upload, Search & Browse Experimental Data43:06
Structured Knowledge Extraction - 143:27
Structured Knowledge Extraction - 243:40
OPPRA - based Gazeteer Knowledge Extraction44:14
Text to Triples44:25
Next Steps44:39
Aboriginal Housing Crisis45:14
Remote, Regional, Metropolitan46:23
Regional/Cultural Factors - 148:15
Regional/Cultural Factors - 248:26
Data Sources48:47
Challenges50:39
Indigenous Housing Ontology51:56
Mapping Interface and R Services52:18
Commonalities53:12
Semantic Knowledge Bases and Decision Support Tools to support Adaptive Management54:35
Semantic Big Data Research Challenges55:02
Acknowledgement – eResearch Lab56:10
Contact56:16