A survey on temporal reasoning in artificial intelligence
AI Communications
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
PATTY: a taxonomy of relational patterns with semantic types
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
Artificial Intelligence
Top-k query processing in probabilistic databases with non-materialized views
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Temporal annotations of facts are a key component both for building a high-accuracy knowledge base and for answering queries over the resulting temporal knowledge base with high precision and recall. In this paper, we present a temporal-probabilistic database model for cleaning uncertain temporal facts obtained from information extraction methods. Specifically, we consider a combination of temporal deduction rules, temporal consistency constraints and probabilistic inference based on the common possible-worlds semantics with data lineage, and we study the theoretical properties of this data model. We further develop a query engine which is capable of scaling to very large temporal knowledge bases, with nearly interactive query response times over millions of uncertain facts and hundreds of thousands of grounded rules. Our experiments over two real-world datasets demonstrate the increased robustness of our approach compared to related techniques based on constraint solving via Integer Linear Programming (ILP) and probabilistic inference via Markov Logic Networks (MLNs). We are also able to show that our runtime performance is more than competitive to current ILP solvers and the fastest available, probabilistic but non-temporal, database engines.