Recommender systems are one of the most successful and widely used applications of machine learning. Their use cases span a range of industry sectors such as e-commerce, entertainment, and social media. In this post, we focus on a fundamental and effective classical approach to recommender systems, which is neighborhood-based.
Read Full StoryRecommender systems are one of the most successful and widely used applications of machine learning. Their use cases span a range of industry sectors such as e-commerce, entertainment, and social medi...
Read MoreRel and the Relational Knowledge Graph Management System provide an excellent tool for investigating and analyzing seismic data. This project illustrates an example of working with data that is distri...
Read MoreHere at RelationalAI, we're passing out knowledge graphs to trick-or-treaters. Come and get spooked with us as we solve a Halloween logic puzzle in Rel, our relational modeling language. You'll see ho...
Read MoreAs 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...
Read MoreWe’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...
Read MoreCreates the first relational knowledge graph system; adds Bob Muglia to its board
Read MoreThe 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...
Read MoreGraph 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...
Read MoreOur 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...
Read MoreWe 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...
Read MoreValue 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.
Read MoreRelationalAI's full suite of SDKs provides access to API endpoints which allow you to track long-running transactions in our Relational Knowledge Graph Management System (RKGMS). This is more reliable...
Read MoreWe 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...
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. Relatio...
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 rea...
Read MoreSemantic Optimization makes your complex data workloads more efficient, which in turn improves overall system performance and scalability.
Read MoreDovetail 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.
Read MoreThis 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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