Paper Info
Title
Predicting RDF triples in incomplete knowledge bases with tensor factorization
Abstract
On RDF datasets, the truth values of triples are known when they are either explicitly stated or can be inferred using logical entailment. Due to the open world semantics of RDF, nothing can be said about the truth values of triples that are neither in the dataset nor can be logically inferred. By estimating the truth values of such triples, one could discover new information from the database thus enabling to broaden the scope of queries to an RDF base that can be answered, support knowledge engineers in maintaining such knowledge bases or recommend users resources worth looking into for instance. In this paper, we present a new approach to predict the truth values of any RDF triple. Our approach uses a 3-dimensional tensor representation of the RDF knowledge base and applies tensor factorization techniques that take open world semantics into account to predict new true triples given already observed ones. We report results of experiments on real world datasets comparing different tensor factorization models. Our empirical results indicate that our approach is highly successful in estimating triple truth values on incomplete RDF datasets.
Year
DOI
Venue
2012
10.1145/2245276.2245341
SAC
Keywords
Field
DocType
rdf base,truth value,rdf knowledge base,predicting rdf triple,knowledge base,3-dimensional tensor representation,triple truth value,rdf datasets,incomplete knowledge base,incomplete rdf datasets,open world semantics,different tensor factorization model,factor model,3 dimensional,knowledge engineering
Data mining,Monad (category theory),Logical consequence,Computer science,Truth value,Theoretical computer science,Tensor factorization,Knowledge base,RDF Schema,RDF,Semantics
Conference
Citations 
PageRank 
References 
6
0.67
12
Authors
3
Name
Order
Citations
PageRank
Lucas Drumond139524.27
Steffen Rendle2196370.68
Lars Schmidt-Thieme33802216.58