Harnessing the Power of Knowledge Graphs for Language Model Governance
Should we all be making a Knowlege Graph part of our organisation's AI strategy? The ICLR is now recognised as one of the top conferences in deep learning… | 11 comments on LinkedIn
Just couple of hours ago, before my webinar Fraud Detection with GNN I've realised we have the reduction in force in TigerGraph
500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.
500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.
Semantic technology helps manage any industry’s complex knowledge
Ontotext is a software company with offices in Europe and USA. The company was first launched in 2000 as an R&D lab within Sirma group. Today, Ontotext is predominantly focused on software solutio
Kay Liu on LinkedIn: BOND: Benchmarking Unsupervised Outlier Node Detection on Static...
Outlier Node Detection (OND) on graphs is widely used in financial fraudster identification, social network spammer detection, and so on. In NeurIPS 2022, we…
Identifying fraudulent behaviors is becoming increasingly more complex as technology advances and fraudsters constantly evolve new ways to exploit people, companies, and institutions. The complexity grows as companies introduce new channels, platforms, and devices for customers to engage with their brand, manage their accounts, and make transactions.
Graph neural networks (GNN) are increasingly being used to identify suspicious behavior. GNNs can combine graph structures, such as email accounts, addresses, phone numbers, and purchasing behavior to find meaningful patterns and enhance fraud detection.
In this video we will discuss:
- Introduction to TigerGraph
- Fraud Detection Challenges
- Graph Model, Data Exploration, and Investigation
- Visual Rules, Red Flags, and Feature Generation
- TigerGraph Machine Learning Workbench:
- XGBoost with Graph Features
- Graph Neural Network and Explainability
Introducing Amazon Neptune Serverless – A Fully Managed Graph Database that Adjusts Capacity for Your Workloads | Amazon Web Services
Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for […]
How graph technology can help strengthen your organization’s security program by providing the right level of context, visibility, and control over its APIs.
Structured Data | 2022 | The Web Almanac by HTTP Archive
Structured Data chapter of the 2022 Web Almanac covering adoption and year on year change of RDFa, Opne Graph, Twitter, JSON-LD, Microdata, Facebook, Dublin Core, Microformats and microformats2 structured data.
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa...
Announcing GUAC, a great pairing with SLSA (and SBOM)!
#Google GUAC (Graph for Understanding Artifact Composition)
Early stage, yet could change how the industry understands software #supplychains
Free tool brings together sources of #software security metadata
Collection - Ingestion - Collation - Query
Saga: A Platform for Continuous Construction and Serving of...
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to...
Summarizing in figures this excellent systematic survey of 507 papers on the state of #KnowledgeGraphs in #NLP since the first Internet-age KG was announced 10 years ago, in the order of appearance:
Summarizing in figures this excellent systematic survey of 507 papers on the state of #KnowledgeGraphs in #NLP since the first Internet-age KG was announced 10… | 12 comments on LinkedIn
We’ve all seen the signs in front of McDonald’s announcing “Over X Billion Served” and have watched the number rise over the years. But tracking how many burgers are sold every day, month, or year is a relic of the past. Today ask: Do we know where each consumer buys her burgers? At what time? What does she drink with it? What does she do before or after buying a burger? How can we satisfy more of her needs so that she keeps coming back? Datagraphs capture this information, helping to reshape competition in every sector. Leaders must invest in upgrading their data architecture to enable a real-time, comprehensive view of how consumers interact with their products and services so that they can develop unique ways to solve customer problems.
Embrace Complexity — Conclusion Building Your Organisation's Knowledge Graph
A powerful idea has been slowly building for many years now, originally known as the Semantic Web, and then later as Linked Data. This idea has finally... 27 comments on LinkedIn
The Four Principles of Semantic Parsing - DataScienceCentral.com
Learn about the Four Principles of Semantic Parsing: The Parser Principle, The Data Uncertainty Principle, The Data Entropy Principle, and The Principle of Deferred Semantics.