knowgl-large
Maintainer: ibm
66
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The knowgl-large model is a knowledge generation and linking model trained by IBM. It combines data from Wikidata with an extended version of the REBEL dataset to generate triples in the format (subject mention # subject label # subject type) | relation label | (object mention # object label # object type). These generated labels and types can be directly mapped to Wikidata IDs. The model achieves state-of-the-art results on the REBEL dataset for relation extraction.
Similar models include REBEL, a relation extraction model that frames the task as a seq2seq problem, and mREBEL, a multilingual version of REBEL that can handle more relation types and languages.
Model inputs and outputs
Inputs
- A sentence to generate knowledge triples from
Outputs
- One or more knowledge triples in the format
(subject mention # subject label # subject type) | relation label | (object mention # object label # object type), separated by$if there are multiple triples
Capabilities
The knowgl-large model can effectively extract relevant knowledge triples from input text, linking the subject, relation, and object to Wikidata entities. This allows for applications like populating or validating knowledge bases, fact-checking, and other downstream tasks that require extracting structured information from text.
What can I use it for?
The generated knowledge triples from knowgl-large can be used to enrich knowledge bases or power applications that require understanding the relationships between entities mentioned in text. For example, you could use the model to automatically extract facts from scientific literature to build a more comprehensive knowledge graph. The model's ability to link to Wikidata also enables applications like semantic search and question answering.
Things to try
One interesting aspect of the knowgl-large model is its ability to generate multiple relevant triples from a single input sentence. This could be useful for tasks like open-domain question answering, where the model could generate a set of potentially relevant facts to answer a given query. You could experiment with prompting the model with different types of sentences and analyzing the diversity and quality of the generated triples.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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