LivingKnowledge goal is to bring a new quality into search and knowledge management technology for more concise, complete and contextualised search results.
The paper “Time-aware Reasoning in Uncertain Knowledge Bases” co-written by Y. Wang, M. Yahya and M. Theobald has been presented at 4th Intl. Workshop on Management of Uncertain Data (MUD-2010) in Singapore, on September 13th - 17th, 2010.
Time information is ubiquitous on the Web, and considering temporal constraints among facts extracted from the Web is key for high-precision query answering over time-variant factual data. In this paper, we present a simple and eﬃcient representation model for timedependent uncertainty in combination with ﬁrst-order inference rules and recursive queries over RDF-like knowledge bases. In the spirit of data lineage, the intensional (i.e., rule-based) structure of query answers is reﬂected by Boolean formulas that capture the logical dependencies of each derived answer fact back to its extensional roots (i.e., base facts). Our approach incorporates simple weight aggregations for begin, end and during evidences for base facts, but also generalizes the common possibleworlds semantics known from probabilistic databases to histogram-like conﬁdence distributions for derived facts. In particular, we show that adding time to the latter probabilistic setting adds only a light overhead in comparison to a time-unaware probabilistic setting.