Two-view Graph Neural Networks for Knowledge Graph Completion

To this end, we propose a new KG embedding model, named A knowledge graph (KG) is a network of entity nodes and WGE, to leverage GNNs to capture entity-focused graph structure relationship edges, which can be represented as a collection and relation-focused graph structure for KG completion. of triples in the form of (h, r, t), wherein each triple (h, r, In particular, WGE transforms a given KG into two views. The t) represents a relation r between a head entity h and a tail first view--a single undirected entity-focused graph--only entity t. Here, entities are real-world things or objects such includes entities as nodes to provide the entity neighborhood as music tracks, movies persons, organizations, places and the information. The second view--a single undirected relationfocused like, while each relation type determines a certain relationship graph--considers both entities and relations as nodes, between entities. KGs are used in a number of commercial applications, constructed from constraints (subjective relation, predicate e.g. in such search engines as Google, Microsoft's entity, objective relation), to attain the potential dependence Bing and Facebook's Graph search. They also are useful between two neighborhood relations. Then WGE introduces a resources for many natural language processing tasks such as new encoder module of adopting two vanilla GNNs directly co-reference resolution ([1], [2]), semantic parsing ([3], [4]) on these two graph views to better update entity and relation and question answering ([5], [6]). However, an issue is that embeddings, followed by the decoder module using a weighted KGs are often incomplete, i.e., missing a lot of valid triples score function. In summary, our contributions are as follows: [7].