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The first LinkedData camp took place in Vienna on Monday, Nov 30, and Tuesday, Dec 1, 2009. Thanks to organizers and sponsors it was a great event: interesting keynotes, nice bunch of people and enough Punsch to warm up

I was mainly working with Jelena Jovanovic and Nikola Milikic on the topic of Expert Search and profiling on the Social Semantic Web. We thought this topic is worth being discussed, because an author’s expertise is a good criterion for estimating the quality of Social Web content; and estimating the quality of  Social Web content is an important problem, because everyone can easily publish content on the Social Web (which increases the information overload problem). The fact that the content published on the Social Web tend to become shorter (see microblog posts or status updates) makes content-based quality estimations difficult. The expertise of an author, however, is an indicator which can also be successfully applied on very short content.

During the first day of the camp we discussed the role of  LinkedData in the context of expert search on the Social Web; i.e. how LinkedData can help to estimate the expertise of a user.

One potential of Linked Data in this context is of course that Linked Data allow to interlink the distributed content of  a user.  That means expertise mining  tools can obviously benefit from exploiting Linked Data in order to get a more complete picture about a user, his content and his activities on various platforms.

However, we also discussed how Linked Data can help to mine topics from Social Web content. Expertise is topic-sensitive. That means, if we want to mine expertise from all items generated by a user (or items about a user) we need to estimate the topics of these items (if they are not explicitly defined). Linked Data is exploited by various Named Entity Recognition services such as OpenCalais,  Zemanta or Muddy Boots. However, we are not aware of any approach using Linked Data as background knowledge to mine topics from text (via computing semantic relateness between Linked Data concepts and any input text).

On the second day we thought about expertise heuristics for the Social Web and we came up with the following expertise heuristic categories:

(1) Connection-based heuristics:

These heuristics are based on analysing the explicit/implicit social network of expert candidate. The assumption of this class of heuristics is that experts tend to be connected with other experts.

For example one concrete heuristic belonging to this class would be: Experts about topic X may have more conversations/discussions with other experts about topic X than non-experts have.

(2) Heuristics based on user pragmatics:

These heuristics are based on analysing usage behaviour of expert candidate. The assumption of this class of heuristics is that experts tend to behave similar (in certain situations or certain time intervals).

For example one concrete heuristic belonging to this class would be: Experts tend to receive (and/or answer) more questions than non-experts.

(3) Heuristics based on the semantic of content:

Analyse semantic of content about expert candidates or content published by expert candidates. The assumption of this class of heuristics is that experts about topic X will use a similar vocabulary or writing style if they talk about X.

For example tow concrete heuristics belonging to this class would be: Experts tend to use higher level of detail if they talk about their expertise topic. Therefore, they will use a bigger vocabulary as non-experts when talking about X. Experts may tend to publish “informative content” about their expertise topic rather than questions or advertisement.

Our conclusion after these 2 days was that we see a clear benefit of using Linked Data for mining expertise from Social Web data. Nevertheless, to our best knowledge no systems or tools exist which exploit LinkedData for expert search and explore potential benefits and pitfalls. If anyone is aware of work in this area, please drop me a line.