Query Answering and Ontology Population: an Inductive Approach

author: Claudia d'Amato, University of Bari

You might be experiencing some problems with Your Video player.

*Slides*
0:00	Query Answering and Ontology Population: an Inductive Approach
0:13	Contents
0:40	Introduction & Motivations
2:12	Knowledge Base Representation
2:48	Nearest Neighbor Classification
3:20	Nearest Neighbor Classification
3:47	Technical Problems
4:28	Customization to DLs
5:24	Nearest Neighbor Classification
5:53	Realized k-NN algorithm
6:45	Semi-Distance Measure: Rationale
8:00	Semantic Semi-Dinstance Measure: Denition
9:19	Defining Feature Weight
10:27	Distance Measure: Example
13:23	Experimental Setting
14:26	Semantic Semi-Dinstance Measure: Denition
14:33	Experimental Setting
15:00	Evaluation in terms of standard IR measures
15:48	Outcomes: Discussion
16:30	Additional Evaluation Parameters
17:23	Additional Outcomes
18:07	Additional outcomes: Discussion
18:49	Likelihood of the inductive assertions
19:14	Likelihood of the inductive assertions: Results
20:18	Conclusions & Future Work
20:59	That's all!
21:11	- Questions
21:57	- Questions
23:00	- Questions
25:58	- Questions

Lecture rating

People found this lecture:
Worth seeing
because it is:
Valuable and informative
Well presented
Easily understandable
Acceptably recorded
You need to login to cast your vote.

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.