K-CAP Workshop on Analyzing Social Media to Represent Collective Knowledge

It is a joined initiative of workshop on Collective Knowledge Capturing and Representation (CKCaR'09) and workshop on Social Media Analysis.

For detailed description of each workshop, please follow the appropriate link

September 1, 2009
Redondo Beach, California, USA

Workshop Description

Social media and collective knowledge are strongly connected to Web 2.0 applications. Both are gaining more importance and are perceived as major elements of the World Wide Web of the next generation. The Web 2.0 has introduced a new model of user interactions and encourages people to massively participate in generating and sharing on-line content as well as interacting with each other. This results in a mass amount of data and information available on the Web, created by the collaboration and competition of many individuals. Major sources of such data are associated with social media, which comes in various forms, for example:

Collective Knowledge emerges by processing, combining, analyzing, and integrating information from these sources. Analysis of the social media plays one of the key roles there, as most of the provided content is user-generated.

Some Web 2.0 platforms already utilize this Collective Knowledge of their users to provide better information and services to their customers. Systems like wikipedia, twitter or xing not only provide factual knowledge, but also opinions which can provide important background knowledge for a multitude of applications, like user behavior analysis or recommendation systems. In order to make use of the information stored in social media special analysis techniques are needed, that are tailored to the special character of social online media.

Collective knowledge is not limited only to social media. Industrial media and corporate or specialized (e.g. biological) knowledge bases can also successfully serve as relevant sources of information, but may require slightly different approaches in extracting new knowledge. Integration of multiple data sources and social media is just a prerequisite for creating Collective Knowledge. As named by Tom Gruber, this is Collected Knowledge (or Collected Intelligence). It becomes Collective Knowledge when new levels of understanding of such knowledge emerge, when "wisdom of the masses" creates new values. One of the advantages of the social media is that it contains subjective information and personal opinions, which can enrich the Collective Intelligence, despite being often inaccurate or inconsistent.

The focus of the workshop lies on how new knowledge can be discovered in such datasets, how to effectively use social media analysis and discover important issues there, which processes are to be used to harvest the new knowledge, how and in which order the processes are to be applied, and finally, how the resulting Collective Knowledge can be represented. The workshop brings together researchers and practitioners from a wide area of research related to knowledge management and representation, media analysis, and Semantic Web. It creates the platform for the participants to discuss the issues, exchange ideas and share experience is Social Media Analysis and Collective Knowledge.

Workshop schedule

The following papers will be presented in the workshop on Analyzing Social Media to Represent Collective Knowledge:

Collective Knowledge Capturing and Representation

1. 9:00 - 9:30 - Symeon Papadopoulos, Yiannis Kompatsiaris and Athena Vakali. "Leveraging Collective Intelligence through Community Detection in Tag Networks"
2. 9:30 - 10:00 - Jorge Gracia and Eduardo Mena. "Multiontology Semantic Disambiguation in Unstructured Web Contexts"
3. 10:00 - 10:30 - Andres Garcia-Silva, Martin Szomszor, Harith Alani and Oscar Corcho. "Preliminary Results in Tag Disambiguation using DBpedia"

10:30 - 11:00 Coffee break

Social Media Analysis

4. 11:00 - 11:30 - Jeon-Hyung Kang, Jihie Kim and Erin Shaw. "Profiling Student Groups in Online Discussion with Network Analysis"
5. 11:30 - 12:00 - Elena Frantova and Sabine Bergler. "Automatic Emotion Annotation of Dream Diaries"

Organizers (CKCaR)

Maciej Janik, University of Koblenz-Landau, Germany, janikuni-koblenz.de (main contact)

Ansgar Scherp, University of Koblenz-Landau, Germany, scherpuni-koblenz.de

Yiannis Kompatsiaris, ITI, Thessaloniki, Greece, ikomiti.gr

Organizers (Social Media Analysis)

Heiner Stuckenschmidt (main contact)

University of Mannheim
A5, 6, 68159 Mannheim, Germany
http://ki.informatik.uni-mannheim.de

Vivi Nastase

EML Research gGmbH
Schloss-Wolfsbrunnenweg 33
69118 Heidelberg, Germany
http://www.eml-r.org/english/homes/nastase/

Frederico Freitas

Centro de Informatica
Av. Luis Freire s/n,
Cidade Universitaria - 50740-540 - Recife , Brazil

Program Committee (CKCaR)

Mathieu d'Aquin, Open University, UK

Christopher Brewster, Aston University, UK

Oren Etzioni, University of Washington, USA

Olivier Gerbe, HEC Montreal, Canada

Bettina Hoser, University of Karlsruhe, Germany

Marta Sabou, Open University, UK

Vojtech Svatek, University of Economics, Prague, Czech Republic

Dan G. Tecuci, University of Texas at Austin, USA

Program Committee (Social Media Analysis)

Rabeeh Abbasi, University of Koblenz, Germany

Sofia Angeletou, Open University, UK

Paul Buitelaar, DERI Galway, Ireland

Kai Eckert, University of Mannheim, Germany

Frederico Freitas, University of Pernambuco at Recife, Brasil

Beate Krause, University of Kassel, Germany

Gustavo Lugo, Technical University of Parana, Brasil

Vivi Nastase, EML Research gGmbH, Germany

Simone Paolo Ponzetto, University of Heidelberg, Germany

Maarten de Rijke, University of Amsterdam, the Netherlands

Marta Sabou, Open University, UK

Heiner Stuckenschmidt, University of Mannheim, Germany