REMI: Mining Intuitive Referring Expressions on Knowledge Bases

TL;DR

REMI is a system designed to efficiently mine intuitive, concise, and informative referring expressions from large RDF knowledge bases, outperforming existing methods in speed and user relevance.

Contribution

This paper introduces REMI, a novel system that rapidly mines intuitive referring expressions from knowledge bases, improving speed and relevance over prior approaches.

Findings

REMI finds REs deemed intuitive by users.

REMI is several orders of magnitude faster than inductive logic programming methods.

REMI effectively handles large RDF knowledge bases.

Abstract

A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, i.e., the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.