Curated knowledge graphs (CKGs) play a fundamental role in both academia and industry. They require significant human involvement to pre-define the ontology and cannot quickly adapt to new domains and new data. To solve this problem, open information extraction (OIE) methods are leveraged to automatically extract structure information in the form of non-canonicalized triples <noun phrase, relation phrase, noun phrase> from unstructured text. OIE can be used to create large open knowledge graphs (OKGs). However, noun phrases and relation phrases in such OKGs are not canonicalized, which results in scattered and redundant facts. In order to disambiguate and eliminate redundancy in such OKGs, the task of OKG canonicalization is proposed to cluster synonymous noun phrases and relation phrases into the same group and assign them unique identifiers. Nevertheless, this task is challenging due to the high sparsity and limited information of OKGs. This chapter provides an overview and analysis of the neuro-symbolic techniques used in this task.