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.

International audienceThis paper proposes a graph-based algorithm baptized REDEN for the disambiguation of authors’ names in French literary criticism texts and scientific essays from the 19th century. It leverages knowledge from different Linked Data sources in order to select candidates for each author mention, then performs fusion of DBpedia and BnF individuals into a single graph, and finally decides the best referent using the notion of graph centrality. Some experiments are conducted in order to identify the best size of disambiguation context and to assess the influence on centrality of specific relations represented as edges. This work will help scholars to trace the impact of authors’ ideas across different works and time periods

No abstract

Recent data indicate that the Simian virus 40 (SV40) infection appears to be transmitted in humans independently from early SV40-contaminated antipolio vaccines. Serum antibodies against SV40 large T antigen (Tag) were analyzed in children/adolescents and young adults. To investigate antibodies reacting to SV40 Tag antigens, serum samples (n = 812) from children and young adults were analyzed by indirect ELISAs using specific SV40 Tag mimotopes. Mimotopes were synthetic peptides corresponding to SV40 Tag epitopes. In sera (n = 412) from healthy children up to 17 years old, IgG antibodies against SV40 Tag mimotopes reached an overall prevalence of 15%. IgM antibodies against SV40 Tag were detected in sera of children 6-8 months old confirming and extending the knowledge that SV40 seroconversion occurs early in life. In children/adolescents affected by different diseases (n = 180) SV40 Tag had a prevalence of 18%, being the difference no significant compared to healthy subjects (n = 220; 16%) of the same age. Our immunological data indicate that SV40 circulates in children and young adults, both in healthy conditions and affected by distinct diseases. The IgM detection in sera from healthy children suggests that the SV40 infection/seroconversion occurs early in life (>6 months). Our immunological data support the hypothesis that SV40, or a closely related still unknown polyomavirus, infects humans. The SV40 seroprevalence is lower than common polyomaviruses, such as BKPyV and JCPyV, and other new human polyomaviruses. In addition, our immunological surveillance indicates a lack of association between different diseases, considered herein, and SV40.

This paper describes the publication and linking of (parts of) PAROLE SIMPLE CLIPS (PSC), a large scale Italian lexicon, to the Semantic Web and the Linked Data cloud using the lemon model. The main challenge of the conversion is discussed, namely the reconciliation between the PSC semantic structure which contains richly encoded semantic information, following the qualia structure of the Generative Lexicon theory and the lemon view of lexical sense as a reified pairing of a lexical item and a concept in an ontology. The result is two datasets: one consists of a list of lemon lexical entries with their lexical properties, relations and senses; the other consists of a list of OWL individuals representing the referents for the lexical senses. These OWL individuals are linked to each other by a set of semantic relations and mapped onto the SIMPLE OWL ontology of higher level semantic types.