Expertise modeling has been the subject of extensive research in two main disciplines: Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA approaches build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data there has been a significant shift from static to evolving documents, through micro-contributions. Thus, the existing macro-perspective is no longer sufficient to track the evolution of both knowledge and expertise. In this paper we present a comprehensive, domain-agnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. We showcase its application in the biomedical domain and analyze its performance using two manually created datasets.

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria.

Abstract:Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts' micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles.

Abstract. Expertise modeling has been the subject of extensive research in two main disciplines -Information Retrieval (IR) and Social Network Analysis (SNA). Both IR and SNA techniques build the expertise model through a document-centric approach providing a macro-perspective on the knowledge emerging from large corpus of static documents. With the emergence of the Web of Data, there has been a significant shift from static to evolving documents, characterized by micro-contributions. Thus, the existing macroperspective is no longer sufficient to track the evolution of both knowledge and expertise. The aim of this research is to provide an all-encompassing, domainagnostic model for expertise profiling in the context of dynamic, living documents and evolving knowledge bases. Our approach combines: (i) finegrained provenance, (ii) weighted mappings of Linked Data concepts to expertise profiles, via the application of IR-inspired techniques on microcontributions, and (iii) collaboration networks -to create and enrich expertise profiles in community-centered environments.

Acquiring and managing expertise profiles represents a major challenge in any organization, as often, the successful completion of a task depends on finding the most appropriate individual to perform it. User profiling has been extensively utilised as a basis for recommendation, personalisation and matchmaking systems. Accurate user profile generators can improve interaction and collaboration between researchers working in similar domains but in different locations or organizations. They can also assist with identifying the optimum set of researchers with complementary skills for cross-disciplinary research teams at a given time. The topic of expertise modelling has been the subject of extensive research in two main disciplines: Information Retrieval (IR) and Social Network Analysis (SNA). Traditional IR and SNA expertise profiling techniques rely on large corpora of static documents authored by an expert, such as publications, reports or grants, the content of which remains unchanged due to the static and final nature of such resources.Consequently, such techniques build the expertise model through a document-centric approach that provides only a macro-perspective of the knowledge emerging from such documents.With the emergence of Web 2.0, there has been a significant increase in online collaboration, giving rise to vast amounts of accessible and searchable knowledge in platforms where content evolves through individuals' contributions. This increase in participation provides vast sources of information, from which knowledge and intelligence can be derived for modelling the expertise of contributors. However, with the proliferation of collaboration platforms, there has been a significant shift from static to evolving documents. Wikis or collaborative knowledge bases, predominantly in the biomedical domain, support this shift by enabling authors to incrementally and collaboratively refine the content of the embedded documents to reflect the latest advances in knowledge in the field. Regardless of the domain, the content of these living documents changes via microcontributions made by individuals, thus making the macro-perspective, provided by the document as a whole, no longer adequate for capturing the evolution of knowledge or expertise. Hence, expertise profiling is presented with major challenges in the context of dynamic and evolving knowledge. Thus, the shift from static documents to living documents requires a shift in the way in which expertise profiling is performed.This thesis examines methods for advancing the state of the art in expertise modelling by considering dynamic content; i.e., platforms in which, knowledge evolves through microcontributions. Towards this goal, a novel expertise profiling framework is introduced that provides solutions for expertise modelling in the context of platforms where knowledge is subject to continuous evolution through experts' micro-contributions; i.e., given a series of microiii contributions, the aim is to build an expertise profile for the author of those...