donbr

How to use GDELT

× This site has no affiliated with GDELT (gdeltproject.org), but does provide an interface to some of its data services - specifically its DOC, GEO and Television APIs. These services should not be confused with the GDELT Events databases to which "GDELT" is most closely associated by some, although they are related.

Using this site you can do things like:

search in English for global content in any or all languages on a particular topic search for specific content or published in the past hour from eg. Japan, or in Japanese, or referencing a Japanese location find content based on features and text in its imagery investigate and compare media trends over time

Scaling Knowledge Graphs for Real-Time News Processing: An Ontology-Free Approach

Rob Kalk
Emergent Methods

Source: https://www.youtube.com/watch?v=jQMq9FbkZAI
Title: GraphGeeks Explainer S2 Ep1: Exploring News at Scale with On-the-Fly Graphs

Abstract

Real-time news processing requires scalable and efficient methodologies for ingesting and structuring large volumes of information. This paper presents the approach employed by Emergent Methods in building a large-scale news knowledge graph, processing over a million entities per day. A key innovation is the introduction of on-the-fly subgraphs, allowing targeted domain-specific explorations. Unlike traditional knowledge graphs that rely on predefined ontologies, this system employs an ontology-free approach, leveraging fine-tuned language models to extract relationships dynamically. We discuss the core architecture, including hybrid vector and graph database storage, fine-tuning techniques using Phi-3, and strategies for mi

Hypergraph Summary

This document provides a comprehensive overview of the Constellation Network's Hypergraph, a decentralized network protocol. The Hypergraph utilizes a directed acyclic graph (DAG) structure, enabling parallel transaction processing for superior scalability compared to traditional blockchains. Its layered architecture features a Global L0 layer for final consensus and immutable data storage, and independent, customizable subnetworks called metagraphs that handle specific functions and data types before submitting snapshots to the Global L0. Key themes include the network's innovative consensus mechanism (Proof-of-Reputable Observation), its flexible tokenomics model (Metanomics) for the native DAG token, and the powerful functionality of metagraphs as building blocks for diverse applications. The overall purpose is to detail the structure and function of this novel blockchain alternative, highlighting its advantages in scalability, security, and interoperability.

Hypergraph Network St

Repository URL: https://huggingface.co/spaces/dwb2023/insight

File: README.md

Type: markdown Size: 304 bytes Created: 2025-02-10 01:37:02 UTC Modified: 2025-02-10 01:37:02 UTC

Content:

---

title: Competency Questions - economics & finance author:

Don created: 2025-02-11 tags:
cq
competency
ontology
requirements

Crisis Analysis Pipeline: From GDELT Events to Actionable Insights

Integrated Architecture

flowchart TD
    A[GDELT Raw Data] --> B[Event Extraction]
    B --> C{Temporal Graph Builder}
    C --> D[Neo4j Knowledge Graph]
    D --> E[Replay Mode Analysis]
    D --> F[Counterfactual Testing]

flowchart TD
  %% Home Page Navigation
  subgraph Home["Home Page (app.py)"]
    A[User] -->|Selects analysis mode| B[Sidebar Navigation]
    B --> C{Available Pages}
    C -->|COVID Navigator| Page1
    C -->|COVID Event Graph Explorer| Page2
    C -->|Global Network Analysis| Page3
    C -->|Feb 2025 Navigator| Page4

	import os
	import asyncio
	from prefect import flow, task, get_run_logger
	from prefect.tasks import task_input_hash
	from prefect.blocks.system import Secret, JSON
	from prefect.task_runners import ConcurrentTaskRunner
	from prefect.concurrency.sync import concurrency
	from pathlib import Path
	import datetime
	from datetime import timedelta

	import os
	import asyncio
	from prefect import flow, task, get_run_logger
	from prefect.tasks import task_input_hash
	from prefect.blocks.system import Secret, JSON
	from prefect.task_runners import ConcurrentTaskRunner
	from prefect.concurrency.sync import concurrency
	from pathlib import Path
	import datetime
	from datetime import timedelta

	# -- coding: utf-8 --
	"""prefect-colab-event-mentions.ipynb

	Automatically generated by Colab.

	Original file is located at
	https://colab.research.google.com/drive/1KZRhjRazTGl7tjyG91Y9uJNDyxxcI2CY
	"""

	# Commented out IPython magic to ensure Python compatibility.