## 1 Introduction  
Organizations are in constant demand of individuals with expertise in specific topics and therefore require extensive profiling systems to enable them to locate experts in a particular knowledge area. However, filling out comprehensive profiling systems and keeping them up to date requires extensive manual effort and has proven to be impractical. A research into expertise profiles at IBM has found that after 10 years of repetitive and consistent pressure from the executives, including periodic emails sent to experts to remind them to update their profiles, only 60% of all IBM profiles are kept up-to-date [1]. This clearly indicates that a manual approach to profiling expertise is not sufficient and an automated solution is required to create and maintain expertise profiles, especially in collaboration platforms where knowledge evolves overtime. There is growing recognition that access to the intrinsic expertise information is critical to the efficient running of enterprise operations. For example, the employees of geographically dispersed organizations typically have difficulty in determining what others are doing and which resources can best address their problems. Failure to foster exchange within the knowledge community leads to duplication of effort and an overall reduction in productivity levels. Expertise is not easily identified and is even more difficult to manage on an ongoing basis, which leaves vast resources of tacit knowledge and experience untapped. Online communities and collaboration platforms have emerged as major components of Web 2.0 [2] and the Semantic Web [3] movements, where experts share their knowledge and expertise through contributions to the underlying knowledge base. Collaboration platforms with support for evolving knowledge provide a _dynamic_ environment where content is subject to ongoing _evolution_ through expert contributions. Consequently, knowledge embedded in such platforms is not static as it evolves through incremental refinements to content or _micro-contributions_, e.g., collaborative authoring/editing of parts of reports, open contributions to domain-specific topics, _etc._ Examples of such platforms are generic Wikis (e.g., Wikipedia) or specific collaborative knowledge bases predominantly in the biomedical domain, such as _AlzSWAN_[4]--which captures and manages hypotheses, arguments and counter-arguments in the Alzheimer's Disease domain or _Gene Wiki_[5], a sub-project of Wikipedia which supports discussions on genes. Representing, capturing and finding expertise has been addressed in many different ways by diverse (often complementary) communities. In Information Retrieval the task of \"_Expert Finding_\" assumes the existence of a set of documents and of a set of expertise profiles and aims to find the best matches between the profiles and the documents. The field of Social Network Analysis, on the other hand, looks at the graphs connecting individuals in different contexts to infer their expertise from shared domain-specific topics [6]. Finally, in the Semantic Web domain expertise is captured using ontologies and then inferred from axioms and rules defined over instances of these ontologies. The major issue associated with all these approaches lies in the underlying use and analysis of a _large corpus_ of _static_ documents authored by the experts (typically publications, reports, _etc._). However, in the context provided by the current collaborative knowledge bases, documents are neither static (as they are _continuously_ and _incrementally_ refined) nor lead to large corpora authored by individual experts (usually authors edit a fraction of a document, which is closer to their expertise/interest). In a previous publication [7] we have introduced a model and a methodology--_Semantic and Time-dependent Expertise Profiling (STEP)_--aimed at capturing _micro-contributions_ in the macro-context of the _living_ documents, as well as the _temporality_ of expertise profiles.