Knowledge graph (KG) embedding aims to encode the entities and relations in KG into low dimensional vector space while preserving the inherent structure of KG. To learn better embeddings, three aspects of KG area, i.e., negative sampling, semantic information in single triplets and structural information in relational paths, are extensively studied in the literature. However, since different KGs have complex and distinct patterns, a single model is usually hard to adapt well to different KGs and different downstream tasks. Recently, automated machine learning (AutoML) has exhibited its power in many machine learning tasks. Inspired by the success of AutoML in both academia and industry, we propose AutoKGE in this thesis to address the three aspects in KG area in different ways....[ Read more ]

Knowledge graph (KG) embedding aims to encode the entities and relations in KG into low dimensional vector space while preserving the inherent structure of KG. To learn better embeddings, three aspects of KG area, i.e., negative sampling, semantic information in single triplets and structural information in relational paths, are extensively studied in the literature. However, since different KGs have complex and distinct patterns, a single model is usually hard to adapt well to different KGs and different downstream tasks. Recently, automated machine learning (AutoML) has exhibited its power in many machine learning tasks. Inspired by the success of AutoML in both academia and industry, we propose AutoKGE in this thesis to address the three aspects in KG area in different ways. AutoKGE is not only new to the literature, but also opens up new directions in analyzing and designing the KG embedding models.

In detail, we propose NSCaching, a simply but very efficient method in sampling high-quality negative triplets. In order to keep track of the dynamic distribution of negative triplets in different KGs, we develop an automated version NSCaching (auto) to adapt the sampling schemes. To capture the different semantic information in each triplet, we propose AutoSF to automatically design SFs for distinct KGs regarding the relation patterns. Novel, better and KG-dependent scoring functions are designed through our algorithm. To explore the structural information, we propose NRASE to distill structural information and combine it with semantic information based on the relational path. Formed as a neural architecture search (NAS) problem, the searched models adaptively combine the structural and semantic information in various KG tasks. Extensive experiments demonstrate the effectiveness of the searched models and efficiency of each search algorithms.