What can knowledge graph alignment gain with Neuro-Symbolic learning
  approaches?
Pedro Giesteira Cotovio Ernesto Jimenez-Ruiz Catia Pesquita
Knowledge Graphs (KG) are the backbone of many data-intensive applications
since they can represent data coupled with its meaning and context. Aligning
KGs across different domains and providers is necessary to afford a fuller and
integrated representation. A severe limitation of current KG alignment (KGA)
algorithms is that they fail to articulate logical thinking and reasoning with
lexical, structural, and semantic data learning. Deep learning models are
increasingly popular for KGA inspired by their good performance in other tasks,
but they suffer from limitations in explainability, reasoning, and data
efficiency. Hybrid neurosymbolic learning models hold the promise of
integrating logical and data perspectives to produce high-quality alignments
that are explainable and support validation through human-centric approaches.
This paper examines the current state of the art in KGA and explores the
potential for neurosymbolic integration, highlighting promising research
directions for combining these fields.