REDEN: Named Entity Linking in Digital Literary Editions Using Linked Data Sets

Abstract : This paper proposes a graph-based Named Entity Linking (NEL) algorithm named REDEN for the disambiguation of authors' names in French literary criticism texts and scientific essays from the 19th and early 20th centuries. The algorithm is described and evaluated according to the two phases of NEL as reported in current state of the art, namely, candidate retrieval and candidate selection. REDEN leverages knowledge from different Linked Data sources in order to select candidates for each author mention, subsequently crawls data from other Linked Data sets using equivalence links (e.g., owl:sameAs), and, finally, fuses graphs of homologous individuals into a non-redundant graph well-suited for graph centrality calculation; the resulting graph is used for choosing the best referent. The REDEN algorithm is distributed in open-source and follows current standards in digital editions (TEI) and semantic Web (RDF). Its integration into an editorial workflow of digital editions in Digital humanities and cultural heritage projects is entirely plausible. Experiments are conducted along with the corresponding error analysis in order to test our approach and to help us to study the weaknesses and strengths of our algorithm, thereby to further improvements of REDEN.
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Carmen Brando, Francesca Frontini, Jean-Gabriel Ganascia. REDEN: Named Entity Linking in Digital Literary Editions Using Linked Data Sets. Complex Systems Informatics and Modeling Quarterly, 2016, pp.60 - 80. ⟨10.7250/csimq.2016-7.04⟩. ⟨hal-01396037⟩

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