## 2 Motivation  
Traditional IR techniques analyze a large corpus of _static documents_; _i.e._, once written, documents do not change and remain forever in the same form. Consequently, they take a document-centric approach to expertise profiling and create a _macro-perspective_ of the knowledge embedded in static documents--individuals are associated with topics extracted from documents independently of their actual contribution. Social Network Analysis (SNA), on the other hand, considers graphs connecting individuals in different contexts and infers their expertise from the shared domain-specific topics [6], e.g., researchers co-authoring publications with other researchers accumulate part of their co-authors' expertise, which is extracted from non-coauthored publications. Both techniques rely on _frequency-based_ statistics, such as Term Frequency (TF) and Inverse Document Frequency (IDF) in the case of IR, or counts of shared in and out links in the case of SNA, to reflect the importance of an item in a document/node in a collection or network. Furthermore, IR methods usually represent documents authored by individuals as bags-of-words and expertise identification is done by associating individual profiles to such bags-of-words--either by ranking candidates based on their similarities to a given topic or by searching for co-occurrences of both the individual and the given topic, in the set of supporting documents. As a remark, such associations can be used to compute semantic similarities between expertise profiles [9]. However, in the context of _evolving knowledge_, these techniques are insufficient, as micro-contributions generally consist of short fragments of text, which do not provide adequate context. We aim to capture expertise by identifying tacit knowledge in the context of collaboration platforms, and thus focus on a _contribution-oriented_ approach, where expertise topics are associated with an author based on the author's _contributions_ to the underlying knowledge base, rather than expertise topics that emerge from the knowledge base (documents) as a whole [7]. In addition to clearly focusing on individual contributions, we are able to take advantage of the underlying dynamic environment and create _fine-grained time-dependent_ expertise profiles [10]. Tracking the evolution of micro-contributions enables one to monitor the activity performed by individuals, which in turn, provides a way to show not only the change in personal interests over time, but also the maturation process of an expert's knowledge (similar to some extent to the maturation process of scientific hypotheses, from simple ideas to scientifically proven facts). From a technical perspective, building expertise profiles from concepts defined in widely adopted ontologies or Linked Data datasets enables individuals who publish their profile on the Web to be intrinsically integrated within the Linked Data Cloud. This provides \"expertise seekers\" and web crawlers with access to richer, more accurate and more up-to-date profiles and facilitates better consolidation of expertise profiles and a seamless aggregation of communities of experts. From an academic perspective, a shift in the scientific publishing process seems to gain momentum, from the current document-centric approach to a contribution-oriented approach in which the hypotheses or domain-related innovations (in form of short statements) will replace the current publications. Examples of this new trend can be seen via _nano-publications_[11] or _liquid publications_[12]. In this new setting, mapping such micro-contributions to expertise will be essential in order to support the development of reputation metrics. As a note, our current work focuses only on building expertise profiles from micro-contributions, which can then provide the foundations upon which novel trust and reputation models could be applied. Finally, more fine-grained and time-aware \"expertise finders\" would help motivate contributing members by rewarding and recognizing experts' knowledge and expertise.