## 1 Introduction  
Knowledge graph embedding (KGE) has emerged as a powerful deep learning technology that has achieved remarkable success in artificial intelligence. However, its techniques are rarely employed for semantic web applications [1]. Linked open data (LOD) have been rapidly growing over the past years, consisting of 1301 machine-readable datasets with 16,283 links as of May 2021. The resource description framework (RDF) is a graph-based data model for LOD. The RDF Schema (RDF-S) provides mechanisms for describing groups of related resources and relationships between these resources. Web ontology language (OWL) is a semantic annotation markup language developed as a vocabulary extension of RDF. The same instances in different datasets are interlinked with owlsameAs, which is a built-in OWL property. To the best of our knowledge, the research on how semantics are embedded and used to train neural network models to achieve instance matching in LOD is relatively meagre. Although numerous state-of-the-art methods are applicable to the LOD cloud, there are still many ongoing problems in this area. One challenge is the interaction between applications using different vocabularies or ontologies. Some annotations face the heterogeneity problem because no standard labelling method applies to all datasets. This problem can occur when identical entities are represented in different ways in multiple datasets. For example, \"Benz E-250\" and \"Mercedes Benz E class 250\" have the same semantics but cannot be matched because they do not belong to the same synonym set. Consequently, an efficient method is needed for semantic searching. Owing to the massive size of the LODcloud, it is practically impossible to perform this operation manually. Therefore, a semantic method for automatic classification and discovery is necessary. Word embedding is a strategy to efficiently represent natural language words in vector space. The purpose of word embedding is to obtain vector values corresponding to each word. Hence, similar words are located at closer distances. This method can be effectively applied to question answering or recommendation systems. Many studies are being conducted for effective word embedding, such as NNLM [2], Word2 Vec [3], and GloVe [4]. Word embedding adopts a preprocessed corpus of text, whereas LOD are stored in the form of RDF graphs. Thus, the LOD cloud must use the graph embedding technique instead of word embedding. The RDF2 Vec [5] is a method for graph embedding that extracts RDF sequence data and uses the Word2 Vec model to embed entities in a low-dimensional space. However, embedded vectors from RDF2 Vec are not directly applied to the semantic search. Although the semantic web community has achieved tremendous success by the LOD project, many researchers argue that semantic technologies have little impact on the real world [6]. In this regard, the KGE technology provided by Google is highly regarded as an example combining concepts and practicalities of the semantic web. The knowledge graph represents the relationships between the head and tail nodes in the form of \(<\)_h_, \(r\), \(l\)-, where \(h\), \(r\), and \(t\) are the head, relation, and tail, respectively. Here, we must consider how the nodes and relations can be represented as vectors. The TransE model [7] proposed that the sum of the head and relation vectors should be maximally equal to the tail. Therefore, the purpose of the score function is to make \(h+r=t\). If all triples satisfy this condition, the nodes and relations can be embedded. The TransE model shows excellent performance in 1:1 relations, but problems are raised in cases such as 1:N, N:1, and N:N. The TransH model [8] solved the TransE problem by projecting the relations onto the hyperplane. In practice, however, entities (head, tail) and relations are different objects, so the TransR model [9] proposed an idea to embed entities and relations in separate spaces.