Author Profile Enrichment for Cross-Linking Digital Libraries

Abstract

Abstract. This work aims at enriching author profiles with additional information to better support search and retrieval of publications across different digital libraries. To achieve this objective we exploit concepts for cross-linking data to identify correlations between one author and other authors, publications or other related information. We will introduce a profile enrichment approach which adds additional information (e.g. biographic information) from different sources to existing author profiles. Within this context, the linked open data repository DBpedia serves a valuable source for our profile enrichment approach. Still, one of several challenges in this context is the identification of the same author in different sources. To address this challenge we will exploit VIAF (virtual authority file) for author identification. Technically we apply data mining and clustering techniques to uniquely identify authors.

Linking science: approaches for linking scientific publications across different LOD repositories

“…The impact can also be seen in the generated results. Concerning this, in our previous work, we have achieved significant results by enriching author profiles with additional information from different digital libraries (Hajra et al, 2015). In another study (Hajra et al, 2014), considering different cases, different combinations of these metadata also led to good results.…”

Enriching Scientific Publications from LOD Repositories Through Word Embeddings Approach

“…Concerning this, in our previous work we have achieved very significant results by enriching author profiles with additional information from different digital libraries [25]. In another study [8], considering different cases, different combinations of these metadata also led to good results.…”

The data set knowledge graph: Creating a linked open data source for data sets

“…The Jaro-Winkler similarity is often used to calculate the similarity of short strings, especially for personal names (Bilenko, Mooney, Cohen, Ravikumar, & Fienberg, 2003). In our implementation, two authors are compared with each other if their names have a Jaro-Winkler similarity of at least 0.9 following Donner (2014) and Hajra, Radevski, and Tochtermann (2015). Concerning the rules for the author name disambiguation, we use factors that have already proven to be reliable in the literature and which are rated according to their importance (Caron & van Eck, 2014;Cen, Dragut, Si, & Ouzzani, 2013;Dendek, Bolikowski, & Lukasik, 2012;Protasiewicz & Dadas, 2016).…”

“…In order to determine whether an author in our knowledge graph is identical to a registered author at ORCID, we compare the authors by means of a variety of features, such as the author's name, the titles of the author's publications, the co-authorship of authors, as well as the identifiers of publications (Hajra et al, 2015;Radevski, Hajra, & Limani, n.d.). A challenge here is that scientific authors usually only list their scientific publications on their public ORCID record, but not published data sets (see Table 13).…”