Using Rel for Machine Learning Data Preprocessing
RelationalAI's declarative modeling language Rel can be a powerful tool for machine learning data preprocessing. It is concise, readable, and facilitates testing and debugging in development. Rel can significantly simplify your machine learning data pipeline.
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View AllUsing Rel for Machine Learning Data Preprocessing
RelationalAI's declarative modeling language Rel can be a powerful tool for machine learning data preprocessing. It is concise, readable, and facilitates testing and debugging in development. Rel can...
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Hybrid and Content-based Recommender Systems in Rel
In this series of blog posts, we show how to implement neighborhood-based, graph-based, content-based, and hybrid recommender systems using RelationalAI’s declarative modeling language, Rel. The impl...
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Graph-based Recommender Systems in Rel
In our previous blog post, we explained how to model traditional neighborhood-based recommender systems in Rel. In what follows, we focus on modeling graph-based recommender systems.
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RelationalAI at the Knowledge Graph Conference
We are delighted to be attending and sponsoring the Knowledge Graph Conference again this year. Last year was an incredible event and this year is lining up to be just as exciting! Our VP of Strategi...
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RelationalAI Year in Review, 2022
As the year draws to a close, we’re taking a look back over 2022 to gather all our best content from the year into one place. It was a great year for us - we came out of stealth, grew our team, partic...
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RelationalAI Named in the 2022 Gartner® Cool Vendors in Augmented Data Management
We’re honored that RelationalAI has been recognized as a Gartner Cool Vendor for Augmented Data Management. We built RelationalAI’s groundbreaking relational knowledge graph system to eliminate knowle...
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How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications: Research by Gartner®
The Gartner report, How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications, published in September 2022, states that “knowledge graphs deliver semantically enabled data management...
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What’s So Special About Graph Analytics?
Graph analytics help us make sense of our connected data by understanding the structure of our data. They help us see which patterns are important and which aren't. They help us predict what’s coming...
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Parsing the Crowded World of Data Analytics: Highlights
Our board member Bob Muglia recently met with Sanjeev Mohan in an interview for the It Depends podcast. Bob and Sanjeev discussed the challenges, trends, technologies and the general pulse of the ever...
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Worksheets in the RAI Console
We are excited to announce worksheets, a new interface for submitting Rel queries. Worksheets allow you to develop blocks of Rel code and run them against a database. They can be shared with other use...
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Varargs in Rel
We are excited to announce the support of varargs in Rel. You can use varargs to write more general code that works for multiple arities. Varargs can be useful when writing generic relations for commo...
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Value Types in Rel
Value types help distinguish between different kinds of values, even though the underlying representation may be identical. Value types can be used to define other value types.
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Building a Named Entity Recognition Model for the Legal Domain
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 el...
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Named Entity Recognition in the Legal Domain
Named 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. Relatio...
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Deep Learning with Relations, NeurIPS 2021
Molham 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 rea...
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Semantic Optimizer
Semantic Optimization makes your complex data workloads more efficient, which in turn improves overall system performance and scalability.
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Dovetail Join
Dovetail Join is a WCOJ (Worst Case Optimal Join) algorithm, meaning we can mathematically prove that the more complicated the problem is, the faster we will go.
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Demo Highlights
This demo is called the “Knowledge Graph to Learn Knowledge Graphs”, or KGLKG. It parallels the authors own journey from programming in imperative languages like Java, C++, and Python to RAI's declara...
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