Abstract Knowledge Graphs (KGs) have made a qualitative leap and effected a real
revolution in knowledge representation. This is leveraged by the underlying structure of the …

Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …

Financial time series analysis plays a central role in hedging market risks and optimizing
investment decisions. This is a challenging task as the problems are always accompanied …

Multimodal Knowledge Graphs (MKGs), which organize visual-text factual knowledge, have
recently been successfully applied to tasks such as information retrieval, question …

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

Data-driven machine learning (ML) is widely employed in the analysis of materials structure–
activity relationships, performance optimization and materials design due to its superior …

Abstract Stock Price Prediction is one of the hot research topics in financial engineering,
influenced by economic, social, and political factors. In the present stock market, the positive …

Stock ranking is a significant and challenging problem. In recent years, the use of multi-view
data, such as price and tweet, for stock ranking has gained considerable attention in the …

Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …