Portfolio
Semantic Knowledge Graphs
Traditional knowledge graphs aim to extract and structure information from general text. Our semantic knowledge graph generation approach leverages not only embedding models used in retreival augmented generated but also link prediction methods to create dense graphs.
Applications include:
Internal knowledge management and discovery
Information discovery such as legal case files
Project management and organisation overviews
SemaDB
SemaDB is a hybrid multi-vector multi-index vector search engine designed to be easy-to-use. It was initially concieved as a component for knowledge management projects. It is now an open-source project with cloud hosting.
Applications include:
Retrieval augmented generation
Semantic similarity search
Hybrid text and vector search
From pixels to logical rules
This work presents a novel method to obtain logical rules directly from end-to-end differentiable neural networks. The overall method can learn logic programs on top of neural perception such as convolutional neural networks.
Applications include:
Post-training inference intervention
Logical grounding of neural networks
Legal
© 2024 Semafind Limited. All rights reserved.
Company
Semafind Ltd.
36 Bruntsfield Place, Edinburgh, United Kingdom, EH10 4HJ
SC745698 - VAT GB435273890