## 9 Related Work
EARS employs two main families of models, both based on generative language modeling techniques, for calculating the probability of a query topic (q) being associated with an entity (e), P(q|e). According to one family of models (Model 1) it builds a textual representation (_i.e._, language model) for each entity, according to the documents associated with that entity. From this representation, it then estimates the probability of the query topic given the entity's language model. In the second group of models (Model 2), it first identifies important documents for a given topic, and then determines which entities are most closely associated with these documents. We have conducted experiments with EARS using our biomedical use cases (see [7]); however, this system also relies on a given set of queries. Furthermore, as with other studies that target expert finding, EARS relies on a _large corpus_ of _static publications_, while we aim at building expert profiles from _micro-contributions_, without relying on any queries. Finally, in the same category of expertise finding, we find SubSift (short for submission sifting), which is a family of RESTful Web services for profiling and matching text [42]. It was originally designed to match submitted conference or journal papers to potential peer reviewers, based on the similarity between the papers' abstracts and the reviewers' publications as found in online bibliographic databases. In this context, the software has already been used to support several major data mining conferences. SubSift, similar to the approaches discussed above, relies on significant amounts of data and uses traditional IR techniques such as TF-IDF, Bag-of-Words and Vector based modeling to profile and compare collections of documents. The ExpertFinder framework uses and extends existing vocabularies that have attracted a considerable user community already such as FOAF, SIOC, SKOS and DublinCore [43]. Algorithms are also proposed for building expertise profiles using Wikipedia by searching for experts via the content of Wikipedia and its users, as well as techniques that use semantics for disambiguation and search extension [40]. We have leveraged these prior efforts to enable the integration of expertise profiles via a shared understanding based on widely adopted vocabularies and ontologies. This approach will also lead to a seamless aggregation of communities of experts. WikiGenes combines a dynamic collaborative knowledgebase for the life sciences with explicit authorship. Authorship tracking technology enables users to directly identify the source of every word. The rationale behind WikiGenes is to provide a platform for the scientific community to collect, communicate and evaluate knowledge about genes, chemicals, diseases and other biomedical concepts in a bottom-up approach. WikiGenes links every contribution to its author, as this link is essential to assess origin, authority and reliability of information. This is especially important in the Wiki model, with its dynamic content and large number of authors [44]. Although WikiGenes links every contribution to its author, it does not associate authors with profiles. More importantly, it does not perform semantic analysis on the content of contributions to extract expertise. As more and more Web users participate in online discussions and micro-blogging, a number of studies have emerged, which focus on aspects such as content recommendation and discovery of users' topics of interest, especially in Twitter. Early results in discovering Twitter users' topics of interest are proposed by examining, disambiguating and categorizing entities mentioned in their tweets using a knowledgebase. A topic profile is then developed, by discerning the categories that appear most frequently and that cover all of the entities [45]. The feasibility of linking individual tweets with news articles has also been analyzed for enriching and contextualizing the semantics of user activities on Twitter in order to generate valuable user profiles for the Social Web [46].