Implement Use Case: Automate Knowledge Graph Triple Generation from Recent Literature with Human-in-the-Loop Validation

[ansh-info]

Is your feature request related to a problem? Please describe.

The current process of updating knowledge graphs (KGs) with information from newly published articles is manual and time-consuming. This delay hampers the timely integration of the latest research findings into our systems, limiting the effectiveness of tools that rely on up-to-date KGs.

Describe the solution you'd like

We propose developing an automated pipeline that:

1. Automated Triple Extraction: Utilizes Large Language Models (LLMs) to extract subject-predicate-object triples from recent scientific literature.

2. Ontology Alignment: Maps extracted triples to existing ontologies to ensure consistency and relevance.

3. Human-in-the-Loop Validation: Implements a validation step where domain experts review and confirm the accuracy of the extracted triples before integration into the KG.Medium

4. Integration into Knowledge Graph: Incorporates validated triples into the existing KG, enhancing its comprehensiveness and utility.arXiv

Describe alternatives you've considered

• Continuing with manual curation, which is not scalable and delays the availability of new information.
• Fully automated integration without human validation, which risks incorporating inaccuracies into the KG.arXiv, CEUR-WS

Additional context

Implementing this pipeline will significantly reduce the time lag between publication and integration of new knowledge into our systems. It will also ensure that the KG remains accurate and up-to-date, thereby improving the performance of downstream applications that depend on it.