2020/12/09 by George Stoica, Stoica, George, Emmanouil Antonios Platanios +3 · 3 citations
Computer Science · #Advanced Graph Neural Networks #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Data mining #FOS: Computer and information sciences #Generality #Graph #Knowledge graph #Leverage (statistics) #Machine learning #Natural Language Processing Techniques #Object (grammar) #Relation (database) #Relationship extraction #Sentence #Set (abstract data type) #Theoretical computer science #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2012.04812
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/12/09 · openalex publication_date 2020/12/09 · arxiv updated 2020/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Relation extraction (RE) aims to predict a relation between a subject and an object in a sentence, while knowledge graph link prediction (KGLP) aims to predict a set of objects, O, given a subject and a relation from a knowledge graph. These two problems are closely related as their respective objectives are intertwined: given a sentence containing a subject and an object o, a RE model predicts a relation that can then be used by a KGLP model together with the subject, to predict a set of objects O. Thus, we expect object o to be in set O. In this paper, we leverage this insight by proposing a multi-task learning approach that improves the performance of RE models by jointly training on RE and KGLP tasks. We illustrate the generality of our approach by applying it on several existing RE models and empirically demonstrate how it helps them achieve consistent performance gains.