RelationalAI
01 January 2020
less than a minute read
This paper develops more formally the tensor-decomposition framework for semantic optimization.
Authors: Mahmoud Abo Khamis, Ryan R. Curtin, Benjamin Moseley, Hung Q. Ngo, Xuan Long Nguyen, Dan Olteanu, Maximilian Schleich. 2020.
In ACM Transactions on Database Systems (TODS ‘20). Vol. 45, No. 4, Article 17.
Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables. We refer to these queries as FAQ-AI for short. We present three applications of our FAQ-AI framework to relational machine learning: k-means clustering, training linear support vector machines, and training models using non-polynomial loss.
We defined named entity recognition (NER) in the legal domain and presented our approach towards generating ground truth data. In what follows, we go over the state-of-the-art in the NER domain and elaborate on the experiments we ran and the lessons we learned.
Read MoreNamed entity recognition is a difficult challenge to solve, particularly in the legal domain. Extracting ground truth labels from long, hierarchical documents is often slow and prone to error. RelationalAI proposes a new, scalable algorithm based on the principles of data-centric AI, designed to meet this challenge and generate high-quality annotations with minimal supervision.
Read MoreMolham shares some history of relational databases, trends in modern cloud-native database systems, and the innovations pioneered at RelationalAI to bring deep learning with relations from idea to reality.
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