From Kolflow Project
Meeting description
Name : non-Kick-off meeting (the project has already been kicked off)
Description :
Start Date : 2013-11-07T09:30:00
End Date : 2013-AA-08T15:30:00
Laboratory : LIRIS
Location : Lyon
General information
Slides and talks can be made either in French or in English
Attending:
- Nantes: Pascal Molli, Luis Ibanez, Diego Torres (by visio), Hala Skaf (by visio)
- Nancy: Alice Hermann, Jean Lieber (at Lyon), Yannick and My Thao (by visio)
- Lyon: Amélie Cordier, Pierre-Antoine Champin,
- Nice: Olivier Corby, Rakeb Hasan, Fabien on 8
Locations
- Meeting Address : Bâtiment Nautibus. Salle C1 le jeudi, Salle C2 le vendredi.
Plan d'accès : http://fst-informatique.univ-lyon1.fr/departement/acces-et-plans/
Detailed Schedule
Thurday 7 :
- 9:00-10:00 Task1 Coordination, GDD (Project coordination)
| Id |
deadline |
task |
number |
title |
delivered |
| D43 |
1515 |
Task4 |
4.3 |
Algorithm to provide performance and errors indicators |
1true |
| D23 |
1818 |
Task23 |
2.3 |
Specification of a continuous knowledge extraction system |
1true |
| D44 |
1818 |
Task4 |
4.4 |
Test and evaluation of the alter ego assistant with regard to the scenarios |
1true |
| D53 |
1818 |
Task5 |
5.3 |
Algorithms and architecture for a distributed semantic wiki and a distributed alter-ego assistant |
1true |
| D45 |
2020 |
Task4 |
4.5 |
Algorithm to explain ontology-based processing |
1true |
| D32 |
2424 |
Task3 |
3.2 |
prototype and tests of the interactive semantic inconsistency managing system |
1true |
| D63 |
2424 |
Task6 |
6.3 |
Man-machine collaboration scenarios progress report |
1true |
| D46 |
2626 |
Task4 |
4.6 |
Algorithm to suggest queries and changes to queries |
1true |
| D54 |
2828 |
Task5 |
5.4 |
Algorithm to distribute query-solving |
1true |
| D55 |
3434 |
Task5 |
5.5 |
Algorithm to explain and document the distributed solving, Process language and machine for man-machine coordination |
1true |
| D25 |
3636 |
Task23 |
2.5 |
Continuous extraction, edition and annotation in a semantic wiki ; report advances on this task at the end of the project. |
1true |
| D24 |
3636 |
Task24 |
2.4 |
Dynamic semantic annotation in a semantic wiki: definitions and specifications |
1true |
| D33 |
3636 |
Task3 |
3.3 |
prototype of the extension integrating management of ”human-machine” and ”logical” inconsistencies |
1true |
| D56 |
3636 |
Task5 |
5.6 |
Algorithm adapted to conflict detection |
|
| D57 |
3636 |
Task5 |
5.7 |
Test and evaluation according to the project scenarios |
1true |
| D64 |
3636 |
Task6 |
6.4 |
Man-machine collaboration scenarios progress report |
1true |
- Tour des délivrables
- préparation de l'évaluation du 25 novembre
- 10:30-11:30 Task3 Inferences and Interactions for dealing with Semantic Inconsistencies, Orpailleur (Design and experiment strategies for dealing with Semantic Inconsistencies)
- Discussion on status of deliverables (maximum : 15 minutes) :
| Id |
deadline |
task |
number |
title |
delivered |
| D32 |
2424 |
Task3 |
3.2 |
prototype and tests of the interactive semantic inconsistency managing system |
1true |
| D33 |
3636 |
Task3 |
3.3 |
prototype of the extension integrating management of ”human-machine” and ”logical” inconsistencies |
1true |
- Summary of task 3, by Alice Hermann (30 minutes)
- Belief merging
- Belief revision
- Integrity constraint belief merging
- 14:30-16:00 Task6 Scenarios and Evaluations, GDD (project evaluation)
- Discussion on status of deliverables:
- Présentation Diego Torres par Skype sur "BlueFinder"
- 16:30-18:00 Task2 Continuous extraction, edition, and annotation, Orpailleur (extracting knowledge units from texts using data mining)
- Discussion on status of deliverables:
| Id |
deadline |
task |
number |
title |
delivered |
| D23 |
1818 |
Task23 |
2.3 |
Specification of a continuous knowledge extraction system |
1true |
| D25 |
3636 |
Task23 |
2.5 |
Continuous extraction, edition and annotation in a semantic wiki ; report advances on this task at the end of the project. |
1true |
- Présentation Yannick Toussaint/Mi thao.
Friday 8
- 09:00-10:30 Task5 Social semantic space, Silex (Design and experiment of a social semantic space)
- Discussion on status of deliverables:
| Id |
deadline |
task |
number |
title |
delivered |
| D53 |
1818 |
Task5 |
5.3 |
Algorithms and architecture for a distributed semantic wiki and a distributed alter-ego assistant |
1true |
| D54 |
2828 |
Task5 |
5.4 |
Algorithm to distribute query-solving |
1true |
| D55 |
3434 |
Task5 |
5.5 |
Algorithm to explain and document the distributed solving, Process language and machine for man-machine coordination |
1true |
| D57 |
3636 |
Task5 |
5.7 |
Test and evaluation according to the project scenarios |
1true |
| D56 |
3636 |
Task5 |
5.6 |
Algorithm adapted to conflict detection |
|
- Presentation: Luis Ibanez "Writable Linked Data"
- 10:45-12:00 Task4 Traces and explanations: documenting inferences, query solving and interactions, Wimmics (Design and experiment tracing and explanation approaches)
- Discussion on status of deliverables:
| Id |
deadline |
task |
number |
title |
delivered |
| D43 |
1515 |
Task4 |
4.3 |
Algorithm to provide performance and errors indicators |
1true |
| D44 |
1818 |
Task4 |
4.4 |
Test and evaluation of the alter ego assistant with regard to the scenarios |
1true |
| D45 |
2020 |
Task4 |
4.5 |
Algorithm to explain ontology-based processing |
1true |
| D46 |
2626 |
Task4 |
4.6 |
Algorithm to suggest queries and changes to queries |
1true |
Title: Predicting SPARQL query execution time and suggesting SPARQL queries based on query history
In this talk we first discuss the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to predict SPARQL query execution time. We generate the training dataset from real queries collected from DBPedia 3.8 query logs. As features of a SPARQL query, we use the SPARQL query algebra operators and different basic graph pattern types that we generate by clustering the training SPARQL queries. We achieved high accuracy (coefficient of determination value of 0.84) for predicting query execution time. Second, we discuss the problem of suggesting similar SPARQL queries based on query history. Users often need assistance to effectively construct and refine Semantic Web queries. To assist users in constructing and refining SPARQL queries, we provide suggestions of similar queries based on query history. Users can use the suggestions to investigate the similar previous queries and their behaviors.
- 15:30 Coffee Break & End of the day
