Annotated Bibliography on Ontology Matching

Annotated Bibliography on Ontology Matchin

AI-Based Recruiting: The Future Ahead

The Human Resources industry is currently being revolutionized by the automation of tedious and time-consuming aspects of their processes. Since AI paradigms such as deep neural networks and other machine learning methods can make accurate predictions and analyze vast amounts of information, these technologies are suitable for facing some of the major challenges in this domain. We overview here how this industry is changing; from the automatic screening of the candidates to bias removal in most of the processes, through techniques for the automatic discovery of potential employees or new advances for improving the candidate's experience

An overview of current ontology meta-matching solutions

Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm

An Overview of Current Ontology Meta-Matching Solutions

Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm