Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic
inference. HoloClean unifies existing qualitative data repairing approaches, which rely on …

Probabilistic programs are usual functional or imperative programs with two added
constructs:(1) the ability to draw values at random from distributions, and (2) the ability to …

The LogicBlox system aims to reduce the complexity of software development for modern
applications which enhance and automate decision-making and enable their users to evolve …

Populating a database with unstructured information is a long-standing problem in industry
and research that encompasses problems of extraction, cleaning, and integration. Recent …

This paper proposes a unified approach to learning from constraints, which integrates the
ability of classical machine learning techniques to learn from continuous feature-based …

Given a knowledge base (KB), rule mining finds rules such as “If two people are married,
then they live (most likely) in the same place”. Due to the exponential search space, rule …

Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

We present an end-to-end (live) demonstration system called DeepDive that performs
knowledge-base construction (KBC) from hundreds of millions of web pages. DeepDive …