Vectors need Graphs! Embedding vectors are a pivotal tool when using Generative AI. While vectors might initially seem an unlikely partner to graphs, their… | 61 comments on LinkedIn

Editor's Note: This post was written by Tomaz Bratanic from the Neo4j team. Extracting structured information from unstructured data like text has been around for some time and is nothing new. However, LLMs brought a significant shift to the field of information extraction. If before you needed a team of

Discover an innovative roadmap, combining LLMs, KGs, and a potent content creation tool to revolutionize SEO-optimized content generation.

How to develop a conceptual data model, such as an EER diagram, UML Class Diagram, or ORM model? Besides dropping icons here and there on an empty canvas, a few strategies exist for approaching it …

We can think of information existing in a continuous stream or in discrete chunks. Large Language Models (LLMs) fall under the category of continuous knowledge… | 73 comments on LinkedIn

Overcoming the "Reversal Curse" in LLMs with Ontologies: The "Reversal Curse" is a term coined in a recent paper to describe a particular failure of… | 108 comments on LinkedIn

🚀 Exciting News: Introducing "Reasoning on Graphs (RoG)" - Unlocking Next-Level Reasoning for Large Language Models! 📊🧠 We are thrilled to unveil our… | 42 comments on LinkedIn

Little demonstrable progress has been made toward AGI (Artificial General Intelligence) since the term was coined some 20 years ago. In spite of the fantastic breakthroughs in Statistical AI such as AlphaZero, ChatGPT, and Stable Diffusion none of these projects have, or claim to have, a clear path to AGI. In order to expedite the development of AGI it is crucial to understand and identify the core requirements of human-like intelligence as it pertains to AGI. From that one can distill which particular development steps are necessary to achieve AGI, and which are a distraction. Such analysis highlights the need for a Cognitive AI approach rather than the currently favored statistical and generative efforts. More specifically it identifies the central role of concepts in human-like cognition. Here we outline an architecture and development plan, together with some preliminary results, that offers a much more direct path to full Human-Level AI (HLAI)/ AGI.

Three months ago, I had the privilege of hosting the Knowledge Graphs Bootcamp on the O'Reilly Learning Platform, and I'm truly grateful for the overwhelming…

Knowledge graph system logic, the "things" and "relations between things" that  graph theory calls "vertices" (a.k.a. nodes, points, entit...

Building knowledge graphs is supposedly a huge and terrifying project, like fighting dragons or sending humans to Mars. I hear or see it time and time again: Knowledge graphs are too difficult, too time consuming, and too expensive to build.

Retrieval systems aim to identify and return the most relevant information for a given query from a large corpus of data. This could…

It was an honor to present the initial results of the Chat with the Data benchmark last week at the The Alan Turing Institute Knowledge Graph meetup (link to… | 11 comments on LinkedIn

Tired of updating your #scraping templates or feeding full webpages to LLMs? Use #Cypher instead! I was in the same boat just 3 months ago when I was working… | 16 comments on LinkedIn

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Happy to announce PyGraft, a configurable #Python tool to generate both synthetic #schemas and #knowledgeGraphs easily, supporting several RDFS and OWL constructs. Paper: https://t.co/p1Ei3PIhVz Code: https://t.co/ID6gU3elqK (also available on PyPI) @nicolas_hubr @mdaquin

On August 14, 2023, the paper Natural Language is All a Graph Needs by Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu and Yongfeng Zhang hit the arXiv streets and made quite a bang! The paper outlines a model called InstructGLM that adds further evidence that the future of graph representation lea

The Memory Game: Investigating the Accuracy of AI Models in Storing and Recalling Facts … 🧠 ... Comparing LLMs and Knowledge Graph on Factual Knowledge I’m… | 18 comments on LinkedIn

No, this is not the chat a father has with Lucius L. Malfoy when he asks to walk your daughter along a path to the prom.

See how to integrate a language language model with TigerGraph to empower question answering with your connected data.

We propose the LLMs4OL approach, which utilizes Large Language Models (LLMs) for Ontology Learning (OL). LLMs have shown significant advancements in natural language processing, demonstrating their ability to capture complex language patterns in different knowledge domains. Our LLMs4OL paradigm investigates the following hypothesis: \textit{Can LLMs effectively apply their language pattern capturing capability to OL, which involves automatically extracting and structuring knowledge from natural language text?} To test this hypothesis, we conduct a comprehensive evaluation using the zero-shot prompting method. We evaluate nine different LLM model families for three main OL tasks: term typing, taxonomy discovery, and extraction of non-taxonomic relations. Additionally, the evaluations encompass diverse genres of ontological knowledge, including lexicosemantic knowledge in WordNet, geographical knowledge in GeoNames, and medical knowledge in UMLS.

“There is a lot of research on establishing mappings between different ontologies, but hardly any work on how to leverage such mappings in a query federation engine. With @Sijin_Cheng and @ferradest, we have embarked on changing that. Paper at @CoopIS2023 https://t.co/vF1emf9R6Z”

Knowledge graphs – a graphic representation of entities in a linked network − have the potential to solve many data integration challenges that pose a significant barrier to the use of AI.

Abstract. We focus on the factuality property during the extraction of an OpenIE corpus named OpenFact, which contains more than 12 million high-quality knowledge triplets. We break down the factuality property into two important aspects—expressiveness and groundedness—and we propose a comprehensive framework to handle both aspects. To enhance expressiveness, we formulate each knowledge piece in OpenFact based on a semantic frame. We also design templates, extra constraints, and adopt human efforts so that most OpenFact triplets contain enough details. For groundedness, we require the main arguments of each triplet to contain linked Wikidata1 entities. A human evaluation suggests that the OpenFact triplets are much more accurate and contain denser information compared to OPIEC-Linked (Gashteovski et al., 2019), one recent high-quality OpenIE corpus grounded to Wikidata. Further experiments on knowledge base completion and knowledge base question answering show the effectiveness of OpenFact over OPIEC-Linked as supplementary knowledge to Wikidata as the major KG.

3 metaphors knowldge graphs to empower us, non-technical people, to be more active in the building of the Web of People.