Constructing experts profiles from Linked Open Data

Identification and assignment of (potential) experts to subject field is an important task in various settings and environments. In scientific domain, the identification of experts is normally based on number of factors like: number of publications, citation record, and experience etc. However, the discovered experts cannot be assigned reviewing duties immediately. One also need further information about expert like the country, university, service record, contributions, honors, and name of conferences/journals where the discovered expert is already serving as editor/reviewer. To some extent, this information can be found from search engines using heuristics, by applying Natural Language Processing, and Machine Learning techniques. However, the emergence of many semantically rich and structured datasets from Linked Open Data movement (LOD) can facilitate in more controlled search and fruitful results. This paper employs an automatic technique to find the required information about experts using LOD dataset. The expert profile is discovered, aggregated, clustered, structured, and visualized to the administration of peer-review system. The system has been implemented for an electronic journal such as Journal of Universal Computer Science (J.UCS). The proposed system facilitates J.UCS administration to find potential reviewers for scientific papers to assign reviewing duties and to call new editors for computer science topics.