Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings (2020)

Abstract: Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vectors’ dimensions, called interactions, are used. These interactions are used to calculate the candidate tail entity vector which is matched against all entities in the KG. However, for most of the existing methods, these interactions are fixed and manually specified. In this work, we propose an automated framework for discovering the interactions while training the KG Embeddings. The proposed method learns relevant interactions along with other parameters during training, allowing it to adapt to different datasets. Many of the existing methods can be seen as special cases of the proposed framework. We demonstrate the effectiveness of the proposed method on link prediction task by extensive experiments on multiple benchmark datasets.

## introduction knowledge graphs (kgs) such as nell @xcite , freebase @xcite , @xcite , etc. have been very popular in supporting many ai applications like web search query recommendation @xcite , question answering @xcite , visual question answering @xcite etc. kgs are multi-relational graphs containing entities as nodes and typed relations between entity pairs as edges. these graphs store real-world facts such as (lionel messi, plays-forteam, argentina national football team) as edges, * work done while at the indian institute of science, bangalore. ## related work the problem of learning kg embeddings for link prediction has been very popular in the last few years. based on the score function, these methods can be broadly grouped into three categories, namely additive, multiplicative and neural models. ## experiment results in this section, we evaluate the proposed method on the link prediction task and compare it against the baselines. the details of the datasets used for evaluation are given in