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Semantic Big Data in Australia - from Dingoes to Drysdale

Published on Nov 28, 20134365 Views

Jane Hunter

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

ISWC 2013 - Sydney

Related categories

Big DataSemantic Web

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