Artificial Intelligence - On connectionist symbol processing
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
PARAFAC: parallel factor analysis
Computational Statistics & Data Analysis - Special issue on multiway data analysis—software and applications
Learning Distributed Representations of Concepts Using Linear Relational Embedding
IEEE Transactions on Knowledge and Data Engineering
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
A neural probabilistic language model
The Journal of Machine Learning Research
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Shallow Semantic Parsing Based on FrameNet, VerbNet and PropBank
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Open text semantic parsing using FrameNet and WordNet
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
On the use of automatically acquired examples for all-nouns word sense disambiguation
Journal of Artificial Intelligence Research
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
ISWC '09 Proceedings of the 8th International Semantic Web Conference
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Unsupervised ontology induction from text
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Semantic compositionality through recursive matrix-vector spaces
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature as well as on data from a real-world knowledge base (WordNet). In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation to almost any sentence of free text, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.