Linked Data Quality for Domain-Specific Named-Entity Linking

We present outgoing research whose goal is to assess quality of Linked Data for its usage in domain-specific Named-entity Linking (NEL). NEL is the task of assigning appropriate referents, typically an Uniform Resource Identifier (URI), to mentions of entities (e.g. persons or places) identified in textual documents. Nowadays, many of these approaches strongly rely on Linked Data as knowledge base. However, the scope of the chosen data sets can have an important influence on the performances of NEL as texts often concern specific domains of knowledge. In this paper, we describe LD quality aspects which should be considered for improving NEL in domain-specific contexts, then propose quality metrics and compute them for both French DBpedia and the French National Library (BnF) data sets thereby to discuss the opportunity of using these data sets for the linking of authors in old French Literary digital editions. Our ultimate goal is to improve a Natural Language Processing (NLP) pipeline for the automatic annotation of these texts.