SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval

Knowledge graphs (KGs), which store an extensive number of relational Knowledge Graphs (KGs), e.g., the widely used YAGO [23], Freebase facts (h,,), serve various applications. While [3], DBpedia [2], WordNet [19], have been serving multiple many downstream tasks highly rely on the expressive modeling and downstream applications such as information retrieval [30], recommender predictive embedding of KGs, most of the current KG representation systems [36, 38], natural language processing [32, 34], learning methods, where each entity is embedded as a vector in the multimedia network analysis [31, 35], question answering [14, 16], Euclidean space and each relation is embedded as a transformation, fact checking [15, 17]. To utilize the extensive amount of knowledge follow an entity ranking protocol. On one hand, such an embedding in the KG, many works have studied Knowledge Graph Embedding design cannot capture many-to-many relations. On the other hand, (KGE), which learns low-dimensional representations of entities in many retrieval cases, the users wish to get an exact set of answers and relations of them [10, 21, 26, 27, 29]. Starting from TransE [4], without any ranking, especially when the results are expected to be a group of translation-based methods TransH [28], TransR [13], precise, e.g., which genes cause an illness. Such scenarios are commonly TransD [9], TorusE [6] model the relation as translations between referred to as "set retrieval". This work presents a pioneering entities in the embedding space. However, the translation-based study on the KG set retrieval problem.