Kùzu Inc.

In this next video of our graph databases fundamentals, Semih Salihoğlu is back with a comprehensive account of the history of graph databases. In working with a technology, knowing some of its history offers valuable context and can help obtain a deeper understanding. Did you know that the first database management system in history, called IDS, was based on a data model called the "network model"? It can be thought of as a precursor to modern graph databases, but the most interesting fact is that it predates relational database management systems by almost 10 years! In this video, you'll also find some inspiring and amusing anecdotes related to well-known engineers and researchers in the field of databases. For example, why did Charlie Bachman, the inventor of the first database in history, visit Alan Turing's mother in England? Or why did Ted Codd, who invented the relational data model, not work on the first prototype relational system called System R, but instead on developing a natural language over databases. Give it a watch, and share around 🚀. We hope this sparks your curiosity in the history of graph databases, and don't forget to subscribe to the channel on YouTube! https://lnkd.in/gW5WPkph #graph #database #history

In our latest video, Prof Semih Salihoğlu discusses why Resource Description Framework (RDF) and the standards around it form a knowledge representation and reasoning (KRR) system, and how one can do automatic reasoning with these standards. KRR systems, which are the foundation of what is nowadays referred to as symbolic AI or "good old fashioned AI" systems. We live in an AI-dominated world, and modern AI techniques such as machine learning or LLMs are based on statistical reasoning. While these techniques are proving to be extremely useful, they cannot (yet) explain the reasoning behind their answers, even when they get them correct. It may be that logic-based KRR systems (along with their underlying principles) could play an important role in addressing some of the shortcomings of existing statistics-based AI systems. If we ever see an emergence of hybrid statistical + symbolic AI systems,  RDF and its standards may be a popular choice to build the symbolic components of such systems. We hope this series of two videos on #rdf and #graphs leaves you with plenty of food for thought! Subscribe to our YouTube channel here: https://lnkd.in/g57ZYs8n https://lnkd.in/g7xxgRbi

Thank you David! On 11 July, Kùzu will be hosting an in-person workshop as part of this year's excellent TMLS agenda. If you're interested in working with knowledge graphs, from a scalability, ease-of-use, and interoperability perspective, consider attending this workshop (among others) and spreading the word! Also, sign up for the TMLS conference on 12/14 July while you're at it :). In our in-person workshop, Prashanth Rao will be covering the basics of knowledge graphs and graph databases, how to get up and running with Kùzu, and how to work with your #graph data via some practical examples, including how to integrate your workflow with the larger AI ecosystem in #python. See you there!

Our next video is out! This is the first of a 2-part video in which Prof. Semih Salihoğlu explains about Resource Description Framework (RDF), a popular data model in graph DBMSs. If you're curious about graph modeling and are intimidated by the terminology used (especially when it comes to RDF), this video has you covered! ✅ Fundamentals of RDF: What are resources, URIs, literals, and (subject, predicate, object) triples, and how do these form a graph? ✅ Advantages of using RDF as a data model for complex and irregular domains and its flexibility in handling irregularities and connections between objects ✅ How RDF allows you to query both data and schema information in a uniform way. In the 2nd video (coming soon!), we will also go over RDF's powerful knowledge representation and reasoning capabilities, and their potential in the age of AI. Stay tuned! https://lnkd.in/gZM79U44

We just published another video by Prof. Semih Salihoğlu - this one addresses the burning question of "When do you need a graph database?", and how GDBMSs are different from RDBMSs. If you're coming from the world of relational databases, this is a very pertinent question to ask, and we think you'll find answers :). ✅ Recursive querying and the notion of paths ✅ Flexible join sequences ✅ Querying patterns in data as graph patterns ✅ Answering entity-oriented questions ✅ Modeling complex & irregular domains (where the data is too complex and irregular to structure as tables) pip install kuzu to get started And please subscribe to our YouTube channel and stay tuned for more in this playlist! https://lnkd.in/empa6ysG #graph #database #rdbms

If you're in the Bay Area, join Prashanth Rao at GitHub HQ for the AICamp meetup on June 4th! In our talk there, we'll be exploring some of the ways in which knowledge graphs are used in conjunction with LLMs and graph databases to power RAG applications that provide insights from structured or unstructured data. We'll also go over the basics of graph databases and what Kùzu brings to the table. RSVP at the link below, and share with your friends and colleagues who are interested in learning more about graphs and their intersection with #rag! https://lnkd.in/eKQdFD8E

Our larger goal at Kùzu Inc. is to make graph databases and knowledge graphs accessible to a much larger community, through our focus on ease-of-use, scalability and performance. Toward this end, Semih Salihoğlu is preparing a series of videos introducing the core concepts of #graph databases and their feature set. ✅ If you are new to graph databases, this series is a great way to become familiar with the key concepts to get started with graphs. ✅ If you're familiar with graph data models, this series is a great way to understand the principles behind Kùzu and to begin using it in your projects. The first video in the series is now out! Give it a watch, and share around 🤝🏽. Subscribe to our YouTube channel for more such videos to be released in the coming weeks! https://lnkd.in/gqdGTqYf Video: https://lnkd.in/gsejgP8w Kùzu GitHub: https://lnkd.in/dUXZH6sT

There's so much being said about GraphRAG these days, so we co-wrote our perspectives with Ben Lorica 罗瑞卡 in his newsletter. The aim of this post is to offer a broad overview of the different ways in which knowledge graphs can play a role in building #rag applications. This is still an evolving space and there's much more work to be done, in terms of implementation details, evaluation and benchmarking. Give this a read and do let us know your comments if you're building the next generation of deterministic RAG and AI applications! https://lnkd.in/gAasUaxz #graph #database #rag

📣 New blog post on #RDF and SHACL In this post, we showcase an extension to the well-known RDFLib Python library written by Paco Nathan, that allows you to use Kùzu as the backend to store and query the RDF graph. Many thanks to Paco for his contribution! ✅ The Shapes Constraint Language (SHACL) is a language for validating RDF graphs against a set of conditions. It's used in Python via the pySHACL library, a pure-Python library for validating RDF graphs that sits on top of RDFLib. You can now combine these tools with Kùzu! ✅ Using the extension framework provided (GitHub link below), you can choose the most appropriate query language to analyze your data and query your triples, depending on your workflow and how you want to interface with the graph — you could use SPARQL via RDFLib or Cypher via Kùzu. ✅ Kùzu provides a simple and intuitive interface to load, query and visualize RDF graphs in Cypher, without compromising scalability and performance, because the RDF triples are essentially mapped to Kùzu’s native property graph model. ✅ This is just the tip of the iceberg in terms of the pipelines you can build over your Kùzu RDFGraphs with RDFLib integration. The linked blog post showed only how to use two implementations of RDF standards in Python: SPARQL and SHACL. But you can use others, like OWL-RL too. We hope this post has provided a good starting point for you to explore RDF data models, SHACL, and how to combine them using Kùzu as your graph backend. Feel free to go through our RDFGraphs documentation to learn more! Blog post: https://lnkd.in/ehehhWGp GitHub repo: https://lnkd.in/eG2phhGw Kùzu RDFGraphs docs: https://lnkd.in/eXRqCM3i #graph #database

Byte #12: In this byte, we will showcase the new similarity search functions from Kùzu Inc.'s latest release v0.4.0. The aim is to answer the following question on this simple graph: "Who is the youngest person who bought a coffee maker?" The items (blue) are "espresso machine" and "yoga mat". So, the provided query is *semantically* similar to one of the items, but not exactly matching, making it a candidate for a vector similarity search. From a schema perspective, the person has a name and an age, and the item has a name and a vector embedding, of type ARRAY (fixed length list) that is basically a text embedding of the item name. To answer the above question about coffee makers (even though the data has "espresso machine"), we need our embedding to capture the similarity of the search term and return the most similar nodes in the graph, following which our filter of "youngest person" can be applied. The embeddings are computed via a function `get_embedding` that uses a sentence-transformers embedding model to transform the text to an embedding that will be ingested into Kùzu. This example shows how we used the recent SoTA Arctic family of embedding models from Snowflake. The query that answers our question leverages the new similarity search function `array_cosine_similarity`. The query vector is passed as a parameter ($query_vector), that's then explicitly cast as an ARRAY of floats with dim 384 so as to compute its similarity with the data. And that's it! We obtain the following result ordered by the similarity and age of the person. The term "espresso machine" is more similar (i.e., closer in vector space) to "coffee maker" than to "yoga mat", thus correctly returning Karissa, of age 25 as our result. To summarize, you can now perform similarity search in @kuzudb using cosine similarity, dot product, inner product and cross product. See our docs for the syntax used, and reach out to us with any questions on the approach shown here! pip install kuzu See the full thread describing this sequence of steps here: https://lnkd.in/gyvd7KFK GitHub repo for code to reproduce these results: https://lnkd.in/gBJSe_xB And our docs on similarity search functions: https://lnkd.in/gqTPP3fy #graph #database #vectorsearch