Generating Fine-Grained Open Vocabulary Entity Type Descriptions

TL;DR

This paper presents a dynamic memory network that generates concise, open-vocabulary descriptions for entities in knowledge graphs by utilizing fact embeddings and context, improving the informativeness of entity descriptions.

Contribution

The authors introduce a novel dynamic memory-based architecture that jointly leverages fact embeddings and context for generating entity descriptions, advancing open vocabulary description generation.

Findings

Outperforms strong baseline models in description accuracy

Effectively utilizes fact embeddings for relevant information extraction

Generates more informative and concise entity descriptions

Abstract

While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type. Unfortunately, many knowledge graph entities lack such textual descriptions. In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words. We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.