## 9 Related Work  
Expertise profiling is an active research topic in a wide variety of applications and domains, including biomedical, scientific and education. In this section, we present a brief overview of the related efforts, with particular emphasis on the Information Retrieval (IR) and the Semantic Web domains. The two most popular and well performing approaches in the _TREC_ (Text Retrieval Conference) [34] expert search task are profile-centric and document-centric approaches. These studies use the co-occurrence model and techniques such as Bag-of-Words or Bag-of-Concepts on documents that are typically large and rich in content. Often a weighted, multiple-sized, window-based approach in an IR model is used for association discovery [35]. Alternatively, the effectiveness of exploiting the dependencies between query terms for expert finding is demonstrated [36]. Other studies present solutions through effective use of ontologies and techniques such as spreading to link additional related terms to a user profile by referring to background knowledge [9]. Algorithms have been proposed to find experts in Wikipedia. One such study attempts to find experts in Wikipedia content or among Wikipedia users [37]. It uses semantics from Wordnet and Yago in order to disambiguate expertise topics and to improve the retrieval effectiveness. However, this study is unable to use the standards proposed for the evaluation of retrieval systems, as relevance assessments are required for representing the ground truth for a list of queries. Furthermore, none of the IR evaluation metrics can be used, since relevance judgments are not available on the Wikipedia collection or the list of queries to run. A relevant initiative to this task is the Web People Search task, which was organized as part of the SemEval-2007 [38] evaluation exercise. This task consists of clustering a set of documents that mention an ambiguous person name according to the actual entities referred to using that name. However, the problem here is that the evaluated task is people name disambiguation and not expert finding. The INEX initiative [39], which provides an infrastructure for the evaluation of content-oriented retrieval of XML documents based on a set of topics, is also relevant but does not consider the expert finding task. To accomplish this task, the study aims to build a gold standard via manually and voluntary defined expertise profiles by Wikipedia users. Such studies contribute to the task of expert finding and in the majority of cases, propose methods for finding experts, given a query or knowledge area in which experts are sought. Not only is _expert finding_ a different task to _expert profiling_, but the methods applied in such studies rely on a large corpus of static documents (e.g., publications) and therefore are not suitable in the context of shorter text, such as _micro-contributions_ in the context of _living_ and _evolving_ documents. Another study, which introduces the task of expert profiling, also relies on queries for extracting expert profiles [40]; the first model uses traditional IR techniques to obtain a set of relevant documents for a given knowledge area (query) and aggregates the relevance of those documents that are associated with the  
Figure 10: Micro-contribution content. given person. The second model represents both candidates and knowledge areas (queries) as a set of keywords and the skills of an individual are estimated based on the overlap between these sets. The Entity and Association Retrieval System _(EARS)_[41] is an open source toolkit for entity-oriented search and discovery in large test collections. EARS, implements a generative probabilistic modeling framework for capturing associations between entities and topics. Currently, EARS supports two main tasks: finding entities (\"_which entities are associated with topic X?_\") and profiling entities (\"_what topics is an entity associated with?_\").