REMI: Mining Intuitive Referring Expressions on Knowledge Bases

11/04/2019
by   Luis Galárraga, et al.
11

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/20/2019

Mining Rules Incrementally over Large Knowledge Bases

Multiple web-scale Knowledge Bases, e.g., Freebase, YAGO, NELL, have bee...
research
12/24/2015

RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles

Recently, several large-scale RDF knowledge bases have been built and ap...
research
02/21/2021

Mining EL Bases with Adaptable Role Depth

In Formal Concept Analysis, a base for a finite structure is a set of im...
research
03/06/2020

Uncovering Hidden Semantics of Set Information in Knowledge Bases

Knowledge Bases (KBs) contain a wealth of structured information about e...
research
02/06/2022

Memory Efficient Tries for Sequential Pattern Mining

The rapid and continuous growth of data has increased the need for scala...
research
08/05/2016

Iterative Learning of Answer Set Programs from Context Dependent Examples

In recent years, several frameworks and systems have been proposed that ...
research
06/05/2020

LGML: Logic Guided Machine Learning

We introduce Logic Guided Machine Learning (LGML), a novel approach that...

Please sign up or login with your details

Forgot password? Click here to reset