Reasoning Over Paths via Knowledge Base Completion

This is crucial for the use of large Knowledge bases in many downstream applications. However explaining the predictions given by a KBC algorithm is quite important for several real world use cases. For example in rec-ommender systems, a knowledge graph of users, items and their interactions are used to recommend an item to a user based on the users interactions on several items. The ability to explain and reason on the decision is of critical importance to add knowledge to recommender systems. Similarly in a knowledge graph consisting human biological data such as genes, drugs, symptoms and diseases, it is crucial to know which gene and symptoms were involved in predicting a drug for a disease. This requires automatic extraction and ranking of multi-hop paths between a given source and a target entity from a knowledge graph. Previous work has focused on using path information in knowledge graphs for KBC known as path-based inference (Lao et al., 2011; Gardner et al., 2014; Neelakantan et al., 2015; Das et al., 2017b), in which a model is trained to predict missing links between a given pair of entities taking as input several paths that existed between them. Paths are ranked according to a scoring method and used as features to train the model. Embedding-based inference models (Bordes et al., 2013; Lin et al., 2015; Nickel et al., 2011; Socher et al., 2013; Trouillon et al., 2016) for KBC learn entity and relation embeddings by solving an optimization problem that maximises the plausibility of known facts in the knowledge graph.