Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …

Deep learning and symbolic reasoning are complementary techniques for an intelligent
system. However, principled combinations of these techniques have limited scalability …

We show that relational algebra calculations for incomplete databases, probabilistic
databases, bag semantics and why-provenance are particular cases of the same general …

A multitude of different probabilistic programming languages exists today, all extending a
traditional programming language with primitives to support modeling of complex, structured …

Because of the world wide web, information retrieval systems are now used by millions of
untrained users all over the world. The search engines that perform the information retrieval …

We present a probabilistic relational algebra (PRA) which is a generalization of standard
relational algebra. In PRA, tuples are assigned probabilistic weights giving the probability …

Most databases contain “name constants” like course numbers, personal names, and place
names that correspond to entities in the real world. Previous work in integration of …

The integration of distributed, heterogeneous databases, such as those available on the
World Wide Web, poses many problems. Herer we consider the problem of integrating data …

In an uncertain database, every object o is associated with a probability density function,
which describes the likelihood that o appears at each position in a multidimensional …

Advances in automatic speech recognition allow us to search large speech collections using
traditional information retrieval methods. The problem of\aboutness" for documents| is a …