Multi-task Knowledge Graph Representations via Residual Functions

Adit Krishnan, Mahashweta Das, Mangesh Bendre, Fei Wang, Hao Yang, Hari Sundaram

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose MuTATE, a Multi-Task Augmented approach to learn Transferable Embeddings of knowledge graphs. Previous knowledge graph representation techniques either employ task-agnostic geometric hypotheses to learn informative node embeddings or integrate task-specific learning objectives like attribute prediction. In contrast, our framework unifies multiple co-dependent learning objectives with knowledge graph enrichment. We define co-dependence as multiple tasks that extract covariant distributions of entities and their relationships for prediction or regression objectives. We facilitate knowledge transfer in this setting: tasks → graph, graph → tasks, and task-1 → task-2 via task-specific residual functions to specialize the node embeddings for each task, motivated by domain-shift theory. We show 5% relative gains over state-of-the-art knowledge graph embedding baselines on two public multi-task datasets and show significant potential for cross-task learning.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
EditorsJoão Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
Number of pages14
ISBN (Print)9783031059322
StatePublished - 2022
Event26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022 - Chengdu, China
Duration: May 16 2022May 19 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13280 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022


  • Graph neural networks
  • Knowledge graph embedding
  • Knowledge graphs
  • Multi-task learning
  • Residual learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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