Ontology Modeling with SHACL: SPARQL-based Constraints | LinkedIn
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SHACL-ing the Data Quality Dragon III: A Good Artisan Knows Their Tools
The internals of a SHACL engine — how Ontotext GraphDB validates your data
New Research Proves Knowledge Graphs Drastically Improve Accuracy of Large Language Models on…
Natural language interfaces to databases have long been a holy grail of both industry and academia. Recently, advances in large language…
With Neptune Analytics, AWS combines the power of vector search and graph data | TechCrunch
AWS announced a new capability today called Neptune Analytics that uses vector search to understand the relationships in a graph database.
Knowledge Graphs in industry and beyond: A quick and dirty intro
I'm constantly looking for new ways to explain why and how knowledge graphs are important. A presentation this week led me to formulate a different, mathier… | 29 comments on LinkedIn
Neo4j’s Vector Search: Unlocking Deeper Insights for AI-Powered Applications - Graph Database & Analytics
Neo4j vector search provides a simple approach for quickly finding contextually related information and uncovering hidden relationships.
LLM Ontology-prompting for Knowledge Graph Extraction
Prompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents
Prompting an LLM with an ontology to drive Knowledge Graph extraction from unstructured documents
RAG on knowledge graphs using Zephyr-7B
Introduction
Automated Knowledge Graph Construction using ChatGPT
Extract hidden insights from unstructured data
Scaling deep learning for materials discovery
Nature - A protocol using large-scale training of graph networks enables high-throughput discovery of novel stable structures and led to the identification of 2.2 million crystal structures, of...
Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing1,2,3,4,5,6,7,8,9,10,11. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottlenecked by expensive trial-and-error approaches. Concurrently, deep-learning models for language, vision and biology have showcased emergent predictive capabilities with increasing data and computation12,13,14. Here we show that graph networks trained at scale can reach unprecedented levels of generalization, improving the efficiency of materials discovery by an order of magnitude.
Launching Fluree Version 3: The Next Generation JSON-LD Database
Fluree launches JSON-LD database with features for data collaboration: secure data policies, immutable linked data, & semantic vocabularies.
On the Multiple Roles of Ontologies in Explainable AI
This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.
Relational Deep Learning
A new research area that generalizes graph machine learning and broadens its applicability to a wide set of #AI use cases
Power Tools for Powerful Knowledge Graphs | LinkedIn
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.
Common sense knowledge graphs are slightly different from conventional knowledge graphs, but they share the most important thing: they both capture explicit symbolic knowledge
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…
Common sense knowledge graphs are slightly different from conventional knowledge graphs, but they share the most important thing: they both capture explicit symbolic knowledge
Embeddings + Knowledge Graphs: The Ultimate Tools for RAG Systems
The advent of large language models (LLMs) , trained on vast amounts of text data, has been one of the most significant breakthroughs in…
A Survey of Graph Meets Large Language Model: Progress and Future Directions
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.
The Practical Benefits to Grounding an LLM in a Knowledge Graph
Note: This article and the underlying LLM application were developed with Alexander Gilmore, Associate Consulting Engineer at Neo4j.
Fast-track graph ML with GraphStorm: A new way to solve problems on enterprise-scale graphs | Amazon Web Services
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 […]
VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search
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.
Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning
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.
Clustering Graph Data With K-Medoids
K-medoids is an approach for discovering clusters in data. It is similar to the well-known k-means algorithm.
SHACL-ing the Data Quality Dragon II: Application, Application, Application!
Applying SHACL to your data and handling the output
SHACL-ing the Data Quality Dragon I: the Problem and the Tools
Learn how SHACL can help you tame your unruly data graphs
VloGraph: A Virtual Knowledge Graph Framework for Distributed Security Log Analysis
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.
The ‘Replicator’ dilemma: When mass isn’t enough
Opinion: The Pentagon should match its big investment in attritable autonomy with smarter targeting, the author argues.
Knowledge Graphs enable LLMs to really understand
LLMs vs human understanding
The Nature of Knowledge Graph Predicates
Knowledge graphs are networks of interconnected facts and each fact is made up of two concepts (subject and object) plus a predicate to…
The Nature of Knowledge Graph Nodes
Knowledge graphs are networks of interconnected facts and each fact is made up of two nodes (in subject and object positions) plus a…
Ontology Modeling with SHACL: Getting Started | LinkedIn
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.