  Cross-lingual entity alignment (EA) enables the integration of multiple
knowledge graphs (KGs) across different languages, providing users with
seamless access to diverse and comprehensive knowledge.Existing methods, mostly
supervised, face challenges in obtaining labeled entity pairs. To address this,
recent studies have shifted towards a self-supervised and unsupervised
frameworks. Despite their effectiveness, these approaches have limitations: (1)
they mainly focus on entity features, neglecting the semantic information of
relations, (2) they assume isomorphism between source and target graphs,
leading to noise and reduced alignment accuracy, and (3) they are susceptible
to noise in the textual features, especially when encountering inconsistent
translations or Out-Of-Vocabulary (OOV) problems.
  In this paper, we propose ERAlign, an unsupervised and robust cross-lingual
EA framework that jointly performs Entity-level and Relation-level Alignment
using semantic textual features of relations and entities. Its refinement
process iteratively enhances results by fusing entity-level and relation-level
alignments based on neighbor triple matching. The additional verification
process examines the entities' neighbor triples as the linearized text. This
\textit{Align-and-Verify} pipeline that rigorously assesses alignment results,
achieving near-perfect alignment even in the presence of noisy textual features
of entities. Our extensive experiments demonstrate that robustness and general
applicability of \proposed improved the accuracy and effectiveness of EA tasks,
contributing significantly to knowledge-oriented applications.
