2017
DOI: 10.7717/peerj-cs.121
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ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

Abstract: Motivation. Scientists increasingly rely on intelligent information systems to help them in their daily tasks, in particular for managing research objects, like publications or datasets. The relatively young research field of Semantic Publishing has been addressing the question how scientific applications can be improved through semantically rich representations of research objects, in order to facilitate their discovery and re-use. To complement the efforts in this area, we propose an automatic workflow to co… Show more

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Cited by 12 publications
(8 citation statements)
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“…The automatic text classification allows, for the first time, profiling based on scientific publications and patents along the product-related, technological and clinical value chain. It contrasts the conventional practice of keyword-based topic representation in which specificity and dynamic creation is opposed to clear comparability (Lin et al 2017;Balog et al 2012;Berendsen et al 2013;Silvello et al 2017;Sateli et al 2017;Chen et al 2015). The profiling approach provides the possibility to clearly determine what an expert's main activities are, independent of any case study (e.g.…”
Section: Discussionmentioning
confidence: 96%
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“…The automatic text classification allows, for the first time, profiling based on scientific publications and patents along the product-related, technological and clinical value chain. It contrasts the conventional practice of keyword-based topic representation in which specificity and dynamic creation is opposed to clear comparability (Lin et al 2017;Balog et al 2012;Berendsen et al 2013;Silvello et al 2017;Sateli et al 2017;Chen et al 2015). The profiling approach provides the possibility to clearly determine what an expert's main activities are, independent of any case study (e.g.…”
Section: Discussionmentioning
confidence: 96%
“…Further analysis of the impact of imbalance in publications and patents and of the potential of filters and other weightings through scaling is possible. In particular, a larger sample could enable a ranking-based comparison to other approaches such as BMExpert (Wang et al 2015) while the comparison remains difficult due to the variety of previous approaches that are "influenced by different variables and components" (Sateli et al 2017). Furthermore, this would again only allow for ranking comparison without taking into account the validity of the underlying profiling.…”
Section: Limitations and Future Workmentioning
confidence: 99%
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“…There are different types of documents that have been used in the current expert finding systems studies. The most used textual expertise source is publications, which have been used by authors in [36,38,44,48,49,53,58,59,61,62,[66][67][68][70][71][72][73]80] as their main expertise source, while publications have been combined with other expertise sources in [22,40,41,43,44,47,55,60,69,75,88,89]. In addition to publications, reference [42] used movies as expertise evidence.…”
mentioning
confidence: 99%
“…Most current expert finding systems retrieved people like documents [4,38], whereas people are unlike documents and are not directly represented as retrievable elements. The representation of experts based on the strength of relationships among a topic and an expert is called representation based on content-based factors [1,25,73]. A challenge in content-based expert finding systems is that the systems need to go beyond document retrieval, as they are required to retrieve entities (experts) instead of documents.…”
mentioning
confidence: 99%