Querying the Semantic Web with RQL
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: The Semantic Web: an evolution for a revolution
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
Anytime Query Answering in RDF through Evolutionary Algorithms
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Language-model-based ranking for queries on RDF-graphs
Proceedings of the 18th ACM conference on Information and knowledge management
Query Evaluation on Probabilistic RDF Databases
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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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.