The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
On Markov chains for independent sets
Journal of Algorithms
Machine Learning
KnowItNow: fast, scalable information extraction from the web
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Yago: a core of semantic knowledge
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Open information extraction from the web
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Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
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Scalable knowledge harvesting with high precision and high recall
Proceedings of the fourth ACM international conference on Web search and data mining
YAGO2: exploring and querying world knowledge in time, space, context, and many languages
Proceedings of the 20th international conference companion on World wide web
Random walk inference and learning in a large scale knowledge base
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Statistical relational data integration for information extraction
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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Probabilistic knowledge bases are commonly used in areas such as large-scale information extraction, data integration, and knowledge capture, to name but a few. Inference in probabilistic knowledge bases is a computationally challenging problem. With this contribution, we present our vision of a distributed inference algorithm based on conflict graph construction and hypergraph sampling. Early empirical results show that the approach efficiently and accurately computes a-posteriori probabilities of a knowledge base derived from a well-known information extraction system.