Grad.Koll.: Unterschied zwischen den Versionen
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|Titel DE=Using Data Mining to Facilitate User Contributions in the Social Semantic Web | |Titel DE=Using Data Mining to Facilitate User Contributions in the Social Semantic Web | ||
|Titel EN=Using Data Mining to Facilitate User Contributions in the Social Semantic Web | |Titel EN=Using Data Mining to Facilitate User Contributions in the Social Semantic Web | ||
| − | |Beschreibung DE=Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute in the Web | + | |Beschreibung DE=Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute content in the Web, organize and share information, and utilize the collective knowledge of others for discovering new topics, resources and new friends. |
While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system’s adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. | While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system’s adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. | ||
| − | In this talk, we present data mining | + | In this talk, we present data mining techniques to address these problems. We investigate the role of recommender systems to reduce the burden of navigating in large information spaces and to aid the user in contributing to the system. In addition, we study intelligent techniques to combat attacks against social Web application specifically social tagging systems. |
| − | |Beschreibung EN=Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute in the Web | + | |
| + | |Beschreibung EN=Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute content in the Web, organize and share information, and utilize the collective knowledge of others for discovering new topics, resources and new friends. | ||
While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system’s adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. | While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system’s adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. | ||
| − | In this talk, we present data mining | + | In this talk, we present data mining techniques to address these problems. We investigate the role of recommender systems to reduce the burden of navigating in large information spaces and to aid the user in contributing to the system. In addition, we study intelligent techniques to combat attacks against social Web application specifically social tagging systems. |
| + | |||
|Veranstaltungsart=Graduiertenkolloquium | |Veranstaltungsart=Graduiertenkolloquium | ||
|Start=2011/01/14 14:00:00 | |Start=2011/01/14 14:00:00 | ||
| Zeile 17: | Zeile 19: | ||
|Vortragender=Maryam Ramezani | |Vortragender=Maryam Ramezani | ||
|Eingeladen durch=Rudi Studer | |Eingeladen durch=Rudi Studer | ||
| + | |PDF=Ramezani 14 01 11 Graduiertenkolloquium.pdf | ||
|Forschungsgruppe=Wissensmanagement | |Forschungsgruppe=Wissensmanagement | ||
|In News anzeigen=False | |In News anzeigen=False | ||
}} | }} | ||
Aktuelle Version vom 6. Dezember 2010, 10:34 Uhr
Using Data Mining to Facilitate User Contributions in the Social Semantic Web
Veranstaltungsart:
Graduiertenkolloquium
Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute content in the Web, organize and share information, and utilize the collective knowledge of others for discovering new topics, resources and new friends.
While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system’s adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users.
In this talk, we present data mining techniques to address these problems. We investigate the role of recommender systems to reduce the burden of navigating in large information spaces and to aid the user in contributing to the system. In addition, we study intelligent techniques to combat attacks against social Web application specifically social tagging systems.
(Maryam Ramezani)
Start: 14. Januar 2011 um 14:00
Ende: 14. Januar 2011 um 15:00
Im Gebäude 11.40, Raum: 231
Veranstaltung vormerken: (iCal)
Veranstalter: Forschungsgruppe(n) Wissensmanagement
Information: Media:Ramezani 14 01 11 Graduiertenkolloquium.pdf