## 4 STEP Revisited  
_Micro-contributions_ represent _incremental refinements_ by authors to an _evolving_ body of knowledge. Examples of such micro-contributions are edits to a Wikipedia article or a Gene page in _Gene Wiki_[5] or a statement in _WikiGenes_[15] and _OMIM_[16]. Regardless of the platform, we areinterested in capturing the _fine-grained provenance_ of these micro-contributions including the actions that lead to their creation, as well as the macro-context that hosts these contributions; _i.e._, paragraph or section of the document in which they appear. Therefore, in the initial phase of our study, we created the _Fine-grained Provenance Ontology_[17], which combines coarse and fine-grained provenance modeling to capture micro-contributions and their localization in the context of their host living documents. The objective has been to reuse and extend existing, established vocabularies from the Semantic Web that have attracted a considerable user community or are derived from _de facto_ standards. This focus guarantees direct applicability and low entry barriers. _Semantic and Time-dependent Expertise Profiling_ (_STEP_) provides a generic methodology for modeling expertise in the context of evolving knowledge. It consists of three main modules, as depicted in Figure 1; (i) _Concept Extraction_; (ii) _Concept Consolidation_; and (iii) _Profile Creation_. Below, we provide a brief description of each of these modules, and refer the reader to [7] for the complete and detailed overview. The _Concept Extraction_ module aims at _annotating micro-contributions_ with _concepts_ from _domain-specific ontologies_. This can be achieved by utilizing a typical information extraction or semantic annotation process, which is, in principle, domain-dependent. Hence, in order to provide a profile creation framework applicable to any domain, we do not restrict this step to the use of a particular concept extraction tool/technique. As micro-contributions for an expert are annotated with concepts from domain-specific ontologies, clusters containing semantically similar concepts emerge. Every entity identified in a micro-contribution is usually annotated with multiple concepts, as numerous domain-specific ontologies are used in the concept extraction process. Furthermore, concept clusters that represent similar semantics often contain one or more concepts in common. These concepts result from the annotation of terms, which appear more frequently and therefore become members of multiple concept clusters. Other concepts in these clusters convey similar semantics; however, they do not appear as frequently as concepts, which are common to multiple clusters. The _Concept Consolidation_ module aggregates these less prominent concepts with concepts that are manifestations of the same entities and appear more frequently; in other words, concept consolidation detects the intersection of groups of concepts resulting from annotation of _lexically different_, but  
Figure 1: Semantic and time-dependent expertise profiling methodology. _semantically similar_ entities across micro-contributions and uses their union to create \"_Virtual Concepts_\". Hence it provides a more accurate and coherent view over entities identified within micro-contributions. A \"_Virtual Concept_\" represents an _abstract_ entity and contains domain-specific concepts from different ontologies, which are manifestations of the abstract entity. Virtual concepts are the building blocks of expertise profiles generated by STEP and central to _short-term_ and _long-term_ profile creation methods and the visualization and search functionality facilitated by _Profile Explorer_[18], our expertise profile visualization tool, as described in Section 8. The _Profile Creation_ module uses the extracted and consolidated concepts to create expertise profiles. The expertise of an individual is dynamic and usually changes with time. In order to capture the _temporal_ aspect of expertise, we differentiate between _Short Term_ and _Long Term_ profiles.