RelationalAI
02 January 2018
less than a minute read
Introduction of an algebraic modeling language in Datalog for mixed-integer linear optimization models
Conrado Borraz-Sanchez, Diego Klabjan, Emir Pasalic, Molham Aref. 2018.
In Declarative Logic Programming: Theory, Systems, and Applications.
Datalog is a deductive language tailored for easy database access. We introduce an algebraic modeling language in Datalog for mixed-integer linear optimization models. By using this language, data can be easily queried from a database by means of Datalog and combined with models to produce problem instances readily available to solvers, providing an advantage over conventional optimization modeling languages that rely on reading data via plug-in tools or importing data from external sources via standard files.
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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