Applying Markov logic for debugging probabilistic temporal knowledge bases

A probabilistic temporal knowledge base contains facts that are annotated with a time interval and a confidence score. The interval defines the time span for which it can be assumed that the fact is true with a probability that is expressed by the confidence score. Given a probabilistic temporal knowledge base, we propose the use of Markov Logic in combination with Allen’s interval calculus to select the most probable consistent subset of facts by computing the MAP state. We apply our approach on a specific domain of DBpedia, namely the domain of academics. We simulate a scenario of extending a knowledge base automatically in an open setting by adding erroneous facts to the facts stated in DBpedia. Our results in- dicate that we can eliminate a large fraction of these errors without removing too many correctly stated facts