Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8579
Title: Inducing-Space Dirichlet Process Mixture Large-Margin Entity Relationship Inference in Knowledge Bases
Authors: Chatzis, Sotirios P. 
Keywords: Computing methodologies;Mathematics of computing;Machine learning;Probability and statistics;Machine learning approaches;Nonparametric statistics;Logical and relational learning;Inductive logic learning
Category: Electrical Engineering - Electronic Engineering - Information Engineering
Field: Engineering and Technology
Issue Date: Oct-2015
Source: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, Melbourne, VIC, Australia, 1311-1320
Abstract: In this paper, we focus on the problem of extending a given knowledge base by accurately predicting additional true facts based on the facts included in it. This is an essential problem of knowledge representation systems, since knowledge bases typically suffer from incompleteness and lack of ability to reason over their discrete entities and relationships. To achieve our goals, in our work we introduce an inducing space nonparametric Bayesian large-margin inference model, capable of reasoning over relationships between pairs of entities. Previous works addressing the entity relationship inference problem model each entity based on atomic entity vector representations. In contrast, our method exploits word feature vectors to directly obtain high-dimensional nonlinear inducing space representations for entity pairs. This way, we allow for extracting salient latent characteristics and interaction dynamics within entity pairs that can be useful for inferring their relationships. On this basis, our model performs the relations inference task by postulating a set of binary Dirichlet process mixture large-margin classifiers, presented with the derived inducing space representations of the considered entity pairs. Bayesian inference for this inducing space model is performed under the mean-field inference paradigm. This is made possible by leveraging a recently proposed latent variable formulation of regularized large-margin classifiers that facilitates mean-field parameter estimation. We exhibit the superiority of our approach over the state-of-the-art by considering the problem of predicting additional true relations between entities given subsets of the WordNet and FreeBase knowledge bases.
URI: http://ktisis.cut.ac.cy/handle/10488/8579
ISBN: 978-1-4503-3794-6
DOI: 10.1145/2806416.2806499
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

SCOPUSTM   
Citations 20

1
checked on Dec 6, 2018

Page view(s) 50

56
Last Week
1
Last month
2
checked on Dec 12, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.