Abstract

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. The path ranking algorithm (PRA) is one of the most promising approaches to this task. Previous work on PRA usually follows a single-task learning paradigm, building a prediction model for each relation independently with its own training data. It ignores meaningful associations among certain relations, and might not get enough training data for less frequent relations. This paper proposes a novel multi-task learning framework for PRA, referred to as coupled PRA (CPRA). It first devises an agglomerative clustering strategy to automatically discover relations that are highly correlated to each other, and then employs a multi-task learning strategy to effectively couple the prediction of such relations. As such, CPRA takes into account relation association and enables implicit data sharing among them. We empirically evaluate CPRA on benchmark data created from Freebase. Experimental results show that CPRA can effectively identify coherent clusters in which relations are highly correlated. By further coupling such relations, CPRA significantly outperforms PRA, in terms of both predictive accuracy and model interpretability.

Highlights

  • Knowledge bases (KBs) like Freebase (Bollacker et al, 2008), DBpedia (Lehmann et al, 2014), and NELL (Carlson et al, 2010) are extremely useful resources for many NLP tasks (Cucerzan, 2007; Schuhmacher and Ponzetto, 2014)

  • Experimental results show that coupled PRA (CPRA) can effectively identify coherent clusters in which relations are highly correlated

  • 5 Experiments where l is the loss on a training instance. It can be instantiated into a logistic regression (LR) or support vector machine (SVM) version, by respectively defining the loss l as: we present empirical evaluation of CPRA in the KB completion task

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Summary

Introduction

Knowledge bases (KBs) like Freebase (Bollacker et al, 2008), DBpedia (Lehmann et al, 2014), and NELL (Carlson et al, 2010) are extremely useful resources for many NLP tasks (Cucerzan, 2007; Schuhmacher and Ponzetto, 2014) They provide large collections of facts about entities and their relations, typically stored as (head entity, relation, tail entity) triples, e.g., (Paris, capitalOf, France). KB completion, i.e., automatically inferring missing facts by examining existing ones, has attracted increasing attention Approaches to this task roughly fall into three categories: (i) path ranking algorithms (PRA) (Lao et al, 2011); (ii) embedding techniques (Bordes et al, 2013; Guo et al, 2015); and (iii) graphical models such as Markov logic networks (MLN) (Richardson and Domingos, 2006). This paper focuses on PRA, which is interpretable (as opposed to embedding techniques) and requires no external logic rules (as opposed to MLN)

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