AutoKGE: Towards Automated Knowledge Graph Embedding

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "AutoKGE: Towards Automated Knowledge Graph Embedding"

By

Mr. Yongqi ZHANG


Abstract:

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.


Date:                   Monday, 24 February 2020

Time:                   2:30pm - 4:30pm

Zoom Meeting:           https://hkust.zoom.com.cn/j/6313069832

Chairman:               Prof. Shing-Yu Leung (MATH)

Committee Members:      Prof. Lei Chen (Supervisor)
                        Prof. Yangqiu Song
                        Prof. Dit-Yan Yeung
                        Prof. Bing-Yi Jing (MATH)
                        Prof. Guoliang Li (Tsinghua Univ.)