Author-Topic Classification Based on Semantic Knowledge

Abstract:

We propose a novel unsupervised two-phased classification model leveraging from semantic web technologies for discovering common research fields between researchers based on information available from a bibliographic repository and external resources. The first phase performs coarse-grained classification by knowledge disciplines using as reference the disciplines defined in the UNESCO thesaurus. The second phase provides a fine-grained classification by means of a clustering approach combined with external resources. The methodology was applied to the REDI (Semantic Repository of Ecuadorian researchers) project, with remarkable results and thus proving a valuable tool to one of the main REDI’s goals: discover Ecuadorian authors sharing research interests to foster collaborative research efforts.

Año de publicación:

2019

Keywords:

• Linked data
• Data integration
• semantic web
• Data Mining
• Knowledge base
• Query languages
• Author-topic classification

Fuente:

google

scopus