Inductive Relation Inference of Knowledge Graph Enhanced by Ontology Information (2023)

Abstract: The inductive inference of the knowledge graph aims to complete the potential relations between the new unknown entities in the graph. Most existing methods are based on entity-independent features such as graph structure information and relationship information to inference. However, the neighborhood of these new entities is often too sparse to obtain enough information to build these features effectively. In this work, we propose a knowledge graph inductive inference method that fuses ontology information. Based on the enclosing subgraph, we bring in feature embeddings of concepts corresponding to entities to learn the semantic information implicit in the ontology. Considering that the ontology information of entities may be missing, we build a type constraint regular loss to explicitly model the semantic connections between entities and concepts, and thus capture the missing concepts of entities. Experimental results show that our approach significantly outperforms large language models like ChatGPT on two benchmark datasets, YAGO21K-610 and DB45K-165, and improves the MRR metrics by 15.4% and 44.1%, respectively, when compared with the state-of-the-art methods.

## introduction knowledge graphs (kgs) store a large amount of structured real-world knowledge through a set of triples, and they have been widely used in many domains, such as natural language processing @xcite , recommendation systems @xcite , and question answering @xcite . however, even the most knowledge-rich kgs suffer from incompleteness, such as dbpedia @xcite , yago @xcite , and wikidata @xcite . to complete the kgs, knowledge graph inference aims to predict the missing links between entities in kgs. in past studies, most methods @xcite mainly learn the specific embeddings of entities and relations and predict missing links by various mapping operations. since the embeddings in such methods depends on specific entities, it requires that the entities in the graph are fixed, which is referred to as the transductive setting @xcite . however, in fact, new emerging entities are continuously added to real-world kgs over time, such as new users and products in e-commerce knowledge graphs and new molecules in biomedical knowledge graphs @xcite . works adopting the transductive setting often require expensive retraining to make predictions for these added entities. as the amount of data increases this overhead will become unaffordable @xcite . to deal with this problem, inductive inference gains the ability to extend what is learned from training entities to unknown entities by learning entity-independent semantic information @xcite . ## approach we start with task formulation. unlike transductive link prediction which predicts unknown triples on a fixed entity graph, for inductive link prediction, the goal is to predict unknown triples (u, r t , v) in the unseen entity graph by learning from the seen entity graph. the entities in these two graphs are disjoint. specifically, given the seen entity graph g e k and the migratable information (e.g., ontology graph g c and type links between entities and concepts t t ), we optimize the score function f (s, r, t) at training time, where the score reflects the likelihood of the existence of target relation r between target nodes s and t. in testing, combined with the migratable information, we use the score function f to predict the unknown triples (u, r t , v) in the unseen entity graph g eu . ## conclusions in this work, we propose a knowledge graph inductive inference method that combines ontology information. the approach integrates semantic information of ontology by using type information for the initialization of node features. we construct a type-constrained regular loss, which effectively captures the missing ontology information of entities. at the same time, ontology training helps the model to enhance the semantic information obtained from the ontology. experimental results show that our approach achieves state-of-the-art inductive link prediction.