A (knowledge) graph is a relational data representation, expressing relations between entities. The Resource Description Framework (RDF) is a common framework to describe and store relational data [1]. A subject (entity), predicate (relation) and object (entity) are the components of the RDF-triple. An entity/node (in this post, entities and nodes are used interchangeably) in the graph can have a type denoted with an rdf:type predicate. An example of a subject-predicate-object RDF-triple is: Tarantino directed Kill Bill.

Research on knowledge graph (KG) construction, completion, inference, and applications has grown rapidly in recent years since it offers a powerful tool for modeling human knowledge in graph forms. Nodes in KGs denote entities and links represent relations between entities. The basic building blocks of KG are entity-relation triples in form of (subject, predicate, object) introduced by the Resource Description Framework (RDF). Learning representations for entities and relations in low dimensional vector spaces is one of the most active research topics in the field. Entity type offers a valuable piece of information to KG learning tasks. Better results in KG-related tasks have been achieved with the help of entity type. For example, TKRL [1] uses a hierarchical type encoder for KG completion by incorporating entity type information. AutoETER [2] adopts a similar approach but encodes the type information with projection matrices. Based on DistMult [3] and ComplEx [4] embedding, [5] propose an improved factorization model without explicit type supervision.

Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.