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The LarKC team is proud to announce that its tutorial “Scalable Integration and Processing of Linked Data” was accepted at this year’s International World Wide Web Conference 2011 (WWW’11) that will take place from March 28th to April 1st, 2011 in Hyderabad, India.
Abstract of the Tutorial
The goal of this tutorial is to introduce, motivate and [...]

Based on the great success of LarKC’s Early Adopters Tutorials, the next workshop is targeted to drug development and discovery.
The LarKC Pharma Workshop will take place on April 19th and 20th 2011 in Stuttgart. The workshop builds on previous tutorials and is customized for drug development/discovery participants. Having completed this workshop, participants will have the [...]

Source: Think Links

Sunbelt is the annual meeting for  Social Network Analysis researchers. It’s been going on since 1981 (a couple of years before analyzing twitter graphs became hip) and this year it’s being held in Tampa. Two of my colleagues-Julie Birkholz and Shenghui Wang- are attending and presenting some joint work. The abstracts are below. If you’re at Sunbelt be sure to check out their presentations and have a chat.

At a higher level, I think both pieces of work emphasize the importance of using the combination of rich representations of the data underlying networks along with dynamic network analysis. Networks provide a powerful abstraction mechanism but it’s important to be able to situate that abstraction in a rich context. The techniques we are both developing and applying are steps along the way towards enabling these more “situated” network.

Dynamics Of Scientific Collaboration Networks

Groenewegen, Peter; Birkholz, Julie M.; van der Bunt, Gerhard; Groth, Paul

Evolution of scientific research can be considered as a dynamic network of collaborative relations between researchers. Collaboration in science leads to social networks in which authors can gain prominence through research (knowledge production), access to highly regarded field members, or network positions in the collaborative network. While a central position in network terms can be considered a measure of prominence, the same holds for citation scores. Causal evidence on a central position in the network corresponding to prominence in other dimensions such as the number of citations remains open. In this paper collaborative patterns, research interests and citation counts of co‐authoring scientists will be analyzed using SIENA to establish whether network processes, community or interest strategies lead to status in a scientific fields, or vice versa does status lead to collaboration. Results from an analysis of a subfield of computer science will be presented.

Multilevel Longitudinal Analysis For Studying Influence Between Co‐evolving Social And Content Networks

Wang, Shenghui; Groth, Paul; Kleinnijenhuis, Jan; Oegema, Dirk A

The Social Semantic Web has begun to provide connections between users within social networks and the content they produce across the whole of the Social Web. Thus, the Social Semantic Web provides a basis to analyze both the communication behavior of users together with the content of their communication. However, there is little research combining the tools to study communication behaviour and communication content, namely, social network analysis and content analysis. Furthermore, there is even less work addressing the longitudinal characteristics of such a combination. This paper proposes to take into account both the social networks and the communication content networks. We present a general framework for measuring the dynamic bi‐directional influence between co‐evolving social and content networks. We focus on the twofold research question: how previous communication content and previous network structure affect (1) the current communication content and (2) the current network structure. Multilevel time‐series regression models are used to model the influence between variables derived from social networks and content networks. The effects are studied at the group level as well as the level of individual actors. We apply this framework in two use‐cases: online forum discussions and conference publications. By analysing the dynamics involving both social networks and content networks, we obtain a new perspective towards the connection of social behaviour in the social web and the traditional content analysis.

 

 

 

Filed under: academia Tagged: semantic web, social network analysis, sunbelt

Source: Semantic Web world for you

LOD Around The Clock (LATC) logoGoogle recently announced a new project, named the Google Art, which give access to paints from around the world in very high definition. It also provides some information related to these paintings.This is a very cool service but the data is not provided in a machine-friendly way. So we thought it would be nice to have a wrapper exporting in RDF so that this data could be more easily consumed by any semantic-aware application.

The GoogleArt2RDF wrapper offers such a wrapping service for any painting made available through GoogleArt. In order to use it, just copy the name of the artwork and paste it after “http://linkeddata.few.vu.nl/googleart/”. For instance, change “http://www.googleartproject.com/museums/rijks/night-watch” into “http://linkeddata.few.vu.nl/googleart/museums/rijks/night-watch“.

The data is expressed using essentially the FOAF and Dublin Core ontologies. When possible, the resources are linked to DBPedia for the author of the painting and the medium used (oil on canvas, etc). This is a first version of the system which does not yet export all the data from Google, comments and suggestions on how to improve it are much welcome!

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