Automated Scoring Function Design for Knowledge Base Embedding

PhD Thesis Proposal Defence


Title: "Automated Scoring Function Design for Knowledge Base Embedding"

by

Mr. Shimin DI


Abstract:

Designing a proper scoring function is the key to ensure the excellent 
performance of knowledge base (KB) embedding. Recently, the scoring 
function search method introduces the automated machine learning (AutoML) 
technique to design the task-aware scoring function for any given binary 
relational data (a.k.a. knowledge graph, KG), which achieves 
state-of-the-art performance. However, the efficiency and effectiveness of 
the current searching method are still not as good as desired. First, the 
existing method consumes a lot of computational overhead to search for a 
proper scoring function. Second, the existing method can only search the 
scoring function for the given binary relational data, which is a special 
form of general KBs (i.e., N-ary relational data).

In this thesis, we present three steps to progressively perform the 
automated scoring function design for knowledge base embedding. First, we 
propose ERAS, an efficient scoring function search method on the binary 
relational data. We suggest sharing the embeddings among candidate scoring 
functions to avoid repeated embedding training in literature, which 
accelerates the search procedure. However, ERAS cannot well adapt to the 
more complex case, N-ary Relational Data. Therefore, we next propose S2S 
to extend the scoring function search from the binary to the N-ary 
scenario. S2S upgrades the search space of scoring functions and improves 
the search algorithm. Finally, we rethink the data sparsity issue in the 
KB embedding. Compared with other modelings, representing KBs with 
multi-relational hypergraphs is a more natural way to encode facts with 
different arities. Therefore, we propose to design a unified and automated 
graph neural networks framework for KB embedding.


Date:			Monday, 3 May 2021

Time:                  	4:00pm - 6:00pm

Zoom Meeting:		https://hkust.zoom.com.cn/j/3060855400

Committee Members:	Prof. Lei Chen (Supervisor)
 			Prof. James Kwok (Chairperson)
 			Dr. Qifeng Chen
 			Dr. Yangqiu Song


**** ALL are Welcome ****