With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

In the context of mass personalization, the dynamic and erratic nature of customer
requirements (CRs) is increasingly pronounced. Amidst intensifying market competition …

Finance is a knowledge-intensive domain in nature, with its data containing a significant
amount of interconnected information. Constructing a financial knowledge graph is an …

In the domain of Natural Language Processing (NLP), Large Language Models (LLMs) have
demonstrated promise in text-generation tasks. However, their educational applications …

Entity linking poses a longstanding challenge within natural language processing, a
challenge that many studies have sought to address from diverse perspectives. However …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

In this paper, we tackle the challenge of predicting stock movements in financial markets by
introducing Higher Order Transformers, a novel architecture designed for processing …

This paper introduces Data2Neo, an open-source Python library for converting relational
data into knowledge graphs stored in Neo4j databases. With extensive customization …

Instrumental Variable (IV) provides a source of treatment randomization that is conditionally
independent of the outcomes, responding to the challenges of counterfactual and …

Maintaining up-to-date knowledge graphs (KGs) is essential for enhancing the accuracy and
relevance of artificial intelligence (AI) applications, especially with sensitive domains. Yet …