Transformer-based Entity Typing in Knowledge Graphs
We investigate the knowledge graph entity typing task which aims at inferring plausible entity types. In this paper, we propose a novel Transformer-based Entity Typing (TET) approach, effectively encoding the content of neighbors of an entity. More precisely, TET is composed of three different mechanisms: a local transformer allowing to infer missing types of an entity by independently encoding the information provided by each of its neighbors; a global transformer aggregating the information of all neighbors of an entity into a single long sequence to reason about more complex entity types; and a context transformer integrating neighbors content based on their contribution to the type inference through information exchange between neighbor pairs. Furthermore, TET uses information about class membership of types to semantically strengthen the representation of an entity. Experiments on two real-world datasets demonstrate the superior performance of TET compared to the state-of-the-art.
Code (1)
Tasks
Entity TypingKnowledge GraphsSimilar Papers 제목 키워드 기반
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an en…
Contrastive LearningEntity TypingKnowledge GraphsMixture-of-ExpertsContext-aware Entity Typing in Knowledge Graphs
Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities' contextual i…
Entity TypingKnowledge GraphsCOTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing
Knowledge graph entity typing (KGET) aims to infer missing entity type instances in knowledge graphs. Previous research has predominantly centered around leveraging contextual information associated with entities, which …
Entity TypingKnowledge GraphsEntity Type Prediction Leveraging Graph Walks and Entity Descriptions
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic ty…
Entity TypingKnowledge GraphsLanguage ModelingLanguage Modelling+3AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations
Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasT…
Entity TypingKnowledge GraphsRelationType prediction