Varargs in Rel image

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 common utilities.

Value Types in Rel image

Value Types in Rel

Value types help distinguish between different types of values, even though the underlying representation may be identical. Value types can be used to define other value types.

Asynchronous Transactions: Start Now, Check Later image

Asynchronous Transactions: Start Now, Check Later

RelationalAI'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 for long-running transactions, allows transactions to be canceled, and keeps a log of transactions that you can inspect either while they're running or at a later time.

How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications: Research by Gartner® image

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 to power a diverse range of AI applications.”

RelationalAI Year in Review, 2022 image

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, participated in fantastic conferences and events, and we’re excited for everything 2023 will bring. Thank you for reading our blog this year and keeping up with our news. We wish you a very happy new year from all of us at RelationalAI.

Building a Named Entity Recognition Model for the Legal Domain image

Building a Named Entity Recognition Model for the Legal Domain

We defined 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.