GraphNews

Knowledge graphs and ontologies, like any other mission-critical resource, require a laser-sharp focus on architecture, accuracy, and coverage. Essentially all downstream processes rely on them, as does the overall success of any knowledge-centric AI product or company.

I really enjoyed the latest #UnconfuseMe with Bill Gates and Yejin Choi.  Yejin's research is on symbolic knowledge distillation, which means they take large…

The advent of large language models (LLMs) , trained on vast amounts of text data, has been one of the most significant breakthroughs in…

Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.

Note: This article and the underlying LLM application were developed with Alexander Gilmore, Associate Consulting Engineer at Neo4j.

We are excited to announce the open-source release of GraphStorm 0.1, a low-code enterprise graph machine learning (ML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. With GraphStorm, you can build solutions that directly take into account the structure of relationships or interactions between billions […]

Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.

Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle.

K-medoids is an approach for discovering clusters in data. It is similar to the well-known k-means algorithm.

Applying SHACL to your data and handling the output

Learn how SHACL can help you tame your unruly data graphs

The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.

Opinion: The Pentagon should match its big investment in attritable autonomy with smarter targeting, the author argues.

LLMs vs human understanding

Knowledge graphs are networks of interconnected facts and each fact is made up of two concepts (subject and object) plus a predicate to…

Knowledge graphs are networks of interconnected facts and each fact is made up of two nodes (in subject and object positions) plus a…

In the world of Knowledge Graphs, an Ontology is a domain model defining classes and properties. Classes are the types of entities (instances) in the graph and properties are the attributes and relationships between them.

We're excited to announce the partnership between Neo4j and AWS to speed up enterprise AI development and resolve key LLM challenges.

Before gearing up for yet another AI project, ask if it's really the best approach.

The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements. Several recent studies have attempted to predict the emergence of new roles and skills in the labour market from online job ads. This paper aims to present a novel ontology linking business transformation initiatives to occupations and an approach to automatically populating it by leveraging embeddings extracted from job ads and Wikipedia pages on business transformation and emerging technologies topics. To our knowledge, no previous research explicitly links business transformation initiatives, like the adoption of new technologies or the entry into new markets, to the roles needed. Our approach successfully matches occupations to transformation initiatives under ten different scenarios, five linked to technology adoption and five related to business. This framework presents an innovative approach to guide enterprises and educational institutions on the workforce requirements for specific business transformation initiatives.

Graph-based data is ubiquitous in enterprise, across the industry verticals, and increasingly needed for machine learning use cases…

Our state-of-the-art model delivers 10-day weather predictions at unprecedented accuracy in under one minute

Reference Architectures for Graph Databases on AWS - aws-samples/aws-dbs-refarch-graph

A method to convert any text corpus into a Knowledge Graph using Mistral 7B.

Living the dreamt of (Knowledge Graph with Connected Content) Some people find it awkward to talk love and greater meaning when it comes to code or… | 19 comments on LinkedIn

Investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems. And ultimately, to succeed in this AI world, enterprises must… | 23 comments on LinkedIn

Some people say we can use "knowledge graph" almost any way we want. This view is incorrect, counterproductive, and dangerous.

Cracking the code of complex systems. It's not the 'Matrix'. 🕶️ A primer on Networks and Graphs. 🕸️ Networks represent the connections between discrete…

Chaotic mess or meaningful maps? 🌪️🌐 Zooming out reveals the hidden logic beneath. Let's unpack the key structures emerging in high-dimensional…

How Sparql Anything may be a game changer for knowledge graphs