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cybermaggedon

cybermaggedon on Hacker News

Re:Show HN: Pulse-Field – O(N) AI Architecture (12x faster than Transf... on Monday November 24, 2025 @09:54AM (#46034791)
Where did the repository go? It has disappeared.

Show HN: Ontology-driven knowledge graph extraction from text

Posted by cybermaggedon on Monday November 24, 2025 @06:34AM
from the show-hn dept.

TrustGraph now supports automatic knowledge graph construction guided by OWL ontologies. You provide an ontology (OWL/Turtle format or build one in the Workbench editor), point it at your documents, and it extracts entities and relationships that conform to your schema.The problem this solves: generic GraphRAG approaches extract whatever relationships an LLM thinks are relevant, which often...

( Read More... | no comments | 2 points | on HN )
Re:Show HN: TrustGraph – Do More with AI with Less (Open Source AI Inf... on Wednesday October 09, 2024 @02:47PM (#41791273)
Have got a recursive chunker and token chunker integrated currently, all configurable. Can add others. Daniel wrote a great blog about trying different chunking parameters at blog.trustgraph.ai/p/dark-art-of-chunking

Show HN: TrustGraph – Do More with AI with Less (Open Source AI Infrastructure)

Posted by cybermaggedon on Monday October 07, 2024 @08:02AM
from the show-hn dept.

https://github.com/trustgraph-ai/trustgraph

Hi HN,We’re Daniel and Mark, the creators of TrustGraph (https://github.com/trustgraph-ai/trustgraph). TrustGraph is an open source, full end-to-end AI infrastructure that automates knowledge graph building and querying along with modular agent integration. A unique aspect of TrustGraph is that the graph building is a one-time process that builds reusable knowledge cores that can be stored,...

( Read More... | 5 comments | 6 points | on HN )
Re:Hans Gruber Would Have Loved Web3 on Tuesday March 22, 2022 @04:44AM (#30763938)
Awesome, at last blockchain explained using Nakatomi Plaza concepts.
Re:Converting Boolean-Logic Decision Trees to Finite State Machines on Thursday January 09, 2020 @04:51PM (#22006276)
"When analyzing cybersecurity events, the detection algorithm evaluates attributes against boolean expressions to determine whether the event belongs to a class. This article describes converting boolean expressions to finite state machines to permit simpler, high-performance evaluation. The open-source project Cyberprobe features this implementation. Conversion of rules to finite state machine (FSM) and application of the rules in FSM form is implemented in Python. Cyberprobe supports the use of millions of rules, which can be applied at greater than 200k events/second on a single processor core."