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A Semantic Web for End Users
Published on Jul 19, 20138875 Views
For whom are we creating the Semantic Web? As we wrestle with our ontologies, alignments, inference methods, entity extractions and triple stores, it's easy to lose track of the vast majority of users
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Chapter list
End User Semantic Web Applications00:00
Conclusion - 102:25
Back Story03:34
Problem-Driven Agenda04:09
Haystack04:41
Picture of Haystack05:32
Writing a Brain Research Paper06:19
Adding “Things to Do” Region06:57
Revised Environment07:06
Role of Semantic Web - 107:14
Semantic Web Applications08:03
Role of Semantic Web - 209:29
Rest of Talk09:47
Homebrew Databases10:56
Complexities of Everyday Information Management in Nonprofit Organizations12:05
Information Management12:27
Useful Applications12:48
Sophisticated Applications13:00
Applications13:24
Volunteer Coordinators - 113:54
63 Million People14:04
Volunteer Coordinators - 214:15
Things get Crazy14:41
Don't have Database14:49
Your Whirlwind Tour of Homebrew Databases15:06
Method15:19
Collecting Information - 116:12
Collecting Information - 216:21
Challenges with a Multiplicity of Systems18:48
Multiple Entries19:18
Homebrew Database19:32
Types of Systems in the Homebrew Databases20:10
Personal Office Applications as Databases - 120:27
Our network21:46
Personal Office Applications as Databases - 222:13
Paper-Based Databases22:48
Enterprise or Custom Databases24:11
We try to keep up25:28
The Cycle of Reconfiguration25:50
Volunteer Coordinators - 326:25
Improving the Human Factors Aspect26:58
Thanks27:16
I Want my Spreadsheet Database to Work Better27:32
Supercharging Spreadsheets for Data Management28:13
Spreadsheets - 128:22
Spreadsheets - 229:21
Alternative: Related Worksheets29:27
One to Many/Many to Many Relationships30:13
A database with one-to-many and many-to-many30:41
Related Worksheets application at startup30:53
Creating a new worksheet31:07
After entering some simple, tabular data31:15
1st New Concept: Data Typesfor Worksheet Columns31:21
2nd New Concept: Array Types31:40
3rd New Concept: Reference Types - 132:02
3rd New Concept: Reference Types - 232:50
4th New Concept: Relationships are bidirectional - 133:46
4th New Concept: Relationships are bidirectional - 234:20
4th New Concept: Relationships are bidirectional - 334:23
Result: The ability to keep track34:58
User Study - 135:08
User Study - 235:18
User Study - 335:40
User Study - 436:15
Results: Demographics36:45
Results: Correctness and Features Used37:00
Results: Timing37:55
Conclusion - 238:27
A Semantic Web Application?39:09
I Want to Publish My Volunteer Roster on the Web40:04
Web Authoring with Structured Data40:15
Some Web History40:28
Good old days ... early 1990s40:30
Wiki41:08
The Virtuous Cycle of Web Authoring41:18
Structured Data is Better41:38
Epicurious - 142:17
Hotpads42:29
Mere mortals just write text or html42:38
Why?42:42
Goal43:09
Do We Need This?43:29
Approach43:59
Like Spreadsheets44:37
Example: HTML44:55
Generalize to Data45:13
Can This be Done?45:35
Epicurious - 245:45
Epicurious - 345:53
Epicurious - 446:01
Epicurious - Data46:03
Epicurious - Views46:16
Epicurious - Facets46:47
Epicurious - Lenses47:10
Key Primitives of a Data Page47:31
General Enough? - 147:43
General Enough? - 247:46
General Enough? - 348:04
General Enough? - 448:13
General Enough? - 548:23
General Enough? - 648:31
General Enough? - 748:43
General Enough? - 848:53
Impoverished Information Visualization?48:57
Exhibit49:41
Prototype: Exhibit49:47
Usage50:35
Examples50:49
Your Artists Albums50:50
Car Finder50:54
Primitivism: Rites of Spring50:56
Hobby Stores51:00
Science51:09
PhD Theses51:13
Rental Apartments51:19
Data.gov51:23
NGOs51:27
Newspapers51:31
Libraries51:41
Sports51:45
Strange Hobbyists51:49
Veggie Guide to Glasgow51:59
Usage Study52:05
Domains52:26
Data Model52:59
Schema Size (Number of Properties)53:50
Data Format54:14
Single-View Exhibits55:08
Percentage of Schema in Visualization55:35
Authoring by Copying56:26
Scalability56:43
Incentivizing Data57:14
Data Export57:42
David Karger's Publications - 157:43
David Karger's Publications - 258:18
David Karger's Publications - 358:27
Summary - 158:36
Extensions58:56
Wibit Collaborative Authoring in a Wiki59:01
Exhibit in a Wiki: Wibit59:04
Exhibit in a Blog: Datapress59:09
WordPress + datapress59:16
Or Just a Document59:27
A Semantic Web Application?01:00:02
I Can't Handle My Incoming Information Overload01:00:46
End Users Programming Information Stream Handlers01:00:54
Motivation01:01:03
Examples01:01:53
What we need01:02:23
Controlled Natural Language Interface01:03:12
Example 101:04:03
Example 2: Travel Mangement01:05:00
Inside a rule01:05:03
Rules in constrained natural language - 101:05:16
Rules in constrained natural language - 201:05:37
Actions in constrained natural language01:05:52
Study01:05:56
Rule creation study (method)01:06:06
List of rules - 101:06:19
List of rules - 201:06:33
Rule creation study - 101:06:44
Rule creation study - 201:06:51
What we found01:07:05
Average time to complete each rule01:07:24
Perceived difficulty of creating rules01:07:30
Perceived usefulness01:07:40
What would you use atomate for? - 101:07:47
What would you use atomate for? - 201:07:59
Discussion01:08:07
If this then that - 101:08:36
If this then that - 201:08:58
If this then that - 301:09:05
If this then that - 401:09:10
If this then that - 501:09:16
If this then that - 601:09:30
If this then that - 701:09:36
If this then that - 801:09:39
If this then that - 901:09:43
If this then that - 1001:09:51
What’s Wrong With This?01:09:56
SW Challenge: Build SWIFTTT01:10:37
Summary - 201:10:51
Whither ESWC?01:11:31
ESWC Topics01:11:39
Semantic Web - 101:12:03
Semantic Web - 201:12:36
Where Are All the Intelligent Agents? - 101:13:17
Where Are All the Intelligent Agents? - 201:13:25
Bringing Intelligence to Applications01:13:34
Where’s the Science?01:13:54
Karger’sBest ESWC Papers01:14:24
Semantic Web Apps at ESWC01:14:58
Conclusion - 201:15:26
Choose Your Motivation Wisely01:26:17
Hammers vs. Nails01:27:32