Enhancing knowledge graph extraction and validation from scholarly publications using bibliographic metadata

Abstract

We have noticed that many scientists process scholarly publications with advanced neural network-driven machine learning techniques to extract semantic information as if they were an ordinary corpus of natural language texts. In this opinion article, we give more emphasis to the value of Bibliometric-Enhanced Information Retrieval as a major field in such a context. In fact, bibliographic metadata can be easily used to refine information retrieval results without depending too much on very advanced techniques. This will allow the development of knowledge graph construction and refinement algorithms that return high accuracy results with less effort.

Houcemeddine Turki

Medical student

My research interests include the development of a large-scale framework for using open resources and semantic technologies for driving biomedical informatics and research evaluation at a low cost.

Mohamed Ali Hadj Taieb

Assistant professor

My research interests include semantic similarity, semantic relatedness, knowledge representation, Big Data, social media, data management systems and graph embedding.

Mohamed Ben Aouicha

Associate professor

My research interests concern information retrieval, semantic technologies, social media analytics, knowledge representation, Big Data and graph embedding.