Leveraging knowledge graphs for LLMs using LangChain

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brief description of our FOIS2023 paper entitled “a method to improve alignments between domain and foundational ontologies”, focusing on BFO-aligned ontologies

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BioCypher is a framework to simplify the creation of robust biomedical knowledge graphs.

RelationalAI announced today the release of an AI co-processor built for Snowflake, the popular cloud data warehouse provider.

Arrived to SIGMOD in Seattle and it’s an amazing honor that our joint academic/industry work on Property Graph Schema received the Best Industry Paper award.… | 14 comments on LinkedIn

Knowledge graphs are becoming an increasing area of interest in respect to explainability and interpretability of AI. To consolidate my own research and… | 30 comments on LinkedIn

“Link prediction on knowledge graphs is a loosing game, IMHO. Without injecting any new info, you'll only find links similar to those you already had. That's why this work is interesting: injecting external knowledge into link prediction is the only way to find truly new links.”

The K in “neuro-symbolic” stands for “knowledge” * Frank van Harmelen, Learning & Reasoning Group Vrije Universiteit Amsterdam Creative Commons License CC BY 3...

As organizations adopt AI and natural language processing, knowledge graphs used in graph databases open new opportunities.

Prompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents

How we delimit and develop the concept of facts is the key to a deeper, more detailed understanding of knowledge graphs because facts are crucial defining…

This new paper is a wonderful story on how generative AI can be used to help curriculum designers build a Knowledge Space dependency graph: Exploring the MIT… | 11 comments on LinkedIn

One of my co-workers asked the other day if we could use an LLM to track the relationships between characters in his favorite book series…

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolving by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs; 2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering; and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions.

👏 #research Excellent paper: Unifying Large Language Models and Knowledge Graphs: A Roadmap ⁉ What is it important work in the right direction? ☠ Despite…

Discover the fascinating fusion of knowledge graphs and LLMs in Neuro-symbolic AI, unlocking new frontiers of understanding and intelligence.

The telecom virtualization and automation pioneer partners with vector-native database provider KX to enable next generation AI-powered telecom operations

What are the limitations of LLMs, and how to overcome them

🔥✌️The future of knowledge graphs in times of Large Language Model (LLMs).👇 How knowledge will be stored and structured, e.g. by Google, in the future is a… | 15 comments on LinkedIn

The concept of a knowledge graph has, since its (re-)introduction in 2012, come to assume a pivotal role in the development of a range of crucial…

Healthcare knowledge graphs (HKGs) have emerged as a promising tool for organizing medical knowledge in a structured and interpretable way, which provides a comprehensive view of medical concepts and their relationships. However, challenges such as data heterogeneity and limited coverage remain, emphasizing the need for further research in the field of HKGs. This survey paper serves as the first comprehensive overview of HKGs. We summarize the pipeline and key techniques for HKG construction (i.e., from scratch and through integration), as well as the common utilization approaches (i.e., model-free and model-based). To provide researchers with valuable resources, we organize existing HKGs (The resource is available at https://github.com/lujiaying/Awesome-HealthCare-KnowledgeBase) based on the data types they capture and application domains, supplemented with pertinent statistical information. In the application section, we delve into the transformative impact of HKGs across various healthcare domains, spanning from fine-grained basic science research to high-level clinical decision support. Lastly, we shed light on the opportunities for creating comprehensive and accurate HKGs in the era of large language models, presenting the potential to revolutionize healthcare delivery and enhance the interpretability and reliability of clinical prediction.

I’ve been comparing ChatGPT responses using statistical similarity v knowledge representations in the automated text selection process. There are token…

Sharing SPARQL queries in Wikibase! Check it out: https://t.co/3FsC4xVRGlWikibase simplifies working with knowledge graphs by allowing users to share predefined SPARQL queries. It seamlessly integrates into the query service, making data exploration easier.#graphdatabase #data pic.twitter.com/bFVaZSh60t— The QA Company (@TheQACompany) May 31, 2023

The scientific work of members of our Department was published in the monograph "Information Technology for Management: Approaches to Improving Business and Society" published by the Springer. The research concerns the automatic assessment of the quality of Wikipedia articles and the reliability of

The OntoGPT framework and SPIRES tool provide a principled approach to extract knowledge from unstructured text for integration into Knowledge Graphs (KGs), using Large Language Models such as GPT. This methodology enables handling complex relationships, ensures logical consistency, and aligns with predefined ontologies for better KG integration.

A Content Graph case study on how the Data Graphs website is self powered - entirely knowledge graph driven