Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Paraconsistent logic programming
Theoretical Computer Science
Bilattices and the semantics of logic programming
Journal of Logic Programming
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Probabilistic logic programming
Information and Computation
A tutorial on learning with Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Signed Systems for Paraconsistent Reasoning
Journal of Automated Reasoning
A Many Sorted Logic with Possibly Empty Sorts
CADE-11 Proceedings of the 11th International Conference on Automated Deduction: Automated Deduction
Machine Learning
ACM SIGKDD Explorations Newsletter
Collective entity resolution in relational data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Preferred Database Repairs Under Aggregate Constraints
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Partitioning activities for agents
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Consistent query answers on numerical databases under aggregate constraints
DBPL'05 Proceedings of the 10th international conference on Database Programming Languages
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News sources are reliably unreliable. Different news sources may provide significantly differing reports about the same event. Often times, even the same news source may provide widely varying data over a period of time about the same event. Past work on inconsistency management and paraconsistent logics assume that we have "clean" definitions of inconsistency. However, when reasoning about events reported in the news, we need to deal with two unique problems: (i) are two events being reported on the same or are they different? and (ii) what does it mean for two event descriptions to be mutually inconsistent, given that these events are often described using linguistic terms that do not always have a uniquely accepted formal semantics? The answers to these two questions turn out to be closely interlinked. In this paper, we propose a probabilistic logic programming language called PLINI (Probabilistic Logic for Inconsistent News Information) within which users can write rules specifying what they mean by inconsistency in situation (ii) above. We show that PLINI rules can be learned automatically from training data using standard machine learning algorithms. PLINI is a variant of the well known generalized annotated program framework that accounts for similarity of numeric, temporal, and spatial terms occurring in news. We develop a syntax, model theoretic semantics, and fixpoint semantics for PLINI rules, and show how PLINI rules can be used to detect inconsistent news reports.