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The first step is to download the source code that allows you to have all the features. The code is available in https://github.com/atrium-research/T3.4.1_KeywordsTranslation. You can download it in ZIP format or clone it using the git command. In this way, you have all the features available on your computer!

The second step is to choose the article you want to work.
If you want to work with an article in the GoTriple platform, the tool offers a simple interface to do this! You only need to look for the article in the platform (https://www.gotriple.eu/). By default, each article in GoTriple comes with an ID and a list of keywords. Once you have the article ID, go to the next step. 
What if your article is not on the GoTriple platform? No problem, go directly to the next step to learn what you can do!


Now, open a Python notebook (you can use Jupyter or Colab). You can look at the file test_notebook to see a tutorial of how to use the tool. 
In case you are working with a GoTriple article and you have identified the ID (see the previous step), execute the function get_item_by_id with the id of your article as parameter. This formats the abstract and the keywords so that they can be ingested by the other steps of the chain. 

In case your article is not in GoTriple article no problem, we have a solution for you! Execute the function get_item_from_user (see an example in the notebook): this function allows you to manually enter your article data, and automatically formats them in a proper way! You will be asked the article title, the keywords, the abstract. The more info you give, the more keywords will be extracted from your data! But if you don't know some of these things no problem, just enter "unknown" and the tool will work anyway!

Now, it's time to choose which tool you want to use to link the keywords to linked data resources! At the present state, the code gives you two options: the first is using DBPedia Spotlight, a simple and light tool which is specialized for named entity linking. This tool is easy and free, but its performance is poor if keywords are not in English.
The second option is to free the power of LLM! You have two choices here: one is to use Mistral, a free model. This method does not require having an account on the OpenAI API, but it is quite slow (nearly 30 seconds to get the answer) and its performance is of course poorer than that of a GPT model. The code also allows you to use your OpenAI key to ask directly to your favourite GPT. In order to execute any of these methods, look at the guide in test_notebook.ipynb. You can choose if you want to give the LLM the content of the abstract, so that it makes more informed choices based on the context. 

If you have some programming skills, you can contribute to the tool and adapt the function in main_functions.py to use different LLMs!

If you execute any of the functions, you will get as result a set of keywords each with its own link to DBPedia (for DBPediaSpotlight) and Wikidata (for all the methods). If you go to the link you can find information about the concept (an explanation, translation in different languages, link to other resources) which you can use for your purposes!

Paolo Pedinotti

Suppose you have a research article which is written in a language other than English and you have a set of keywords for that article. How could you grasp the main topics of the article from the keywords? First, you would like to translate them, but this is not a trivial task since words can be ambiguous, and finding the exact sense requires an understanding of the whole article! Moreover, suppose you want to link your article to other resources, for example other articles that share the same topic. Now suppose you want to make your resource accessible so that people can find keywords in different languages. This workflow provides a solution to all this scenarios, since it gives you a way to map keywords to linked data (Wikidata and DBPedia entities). 

LLM-Powered Mapping of Keywords of a Research Article to Linked Data

Workflow steps(5)

1 Download the source code

2 Choose the article you want to analyze

3 Format Keywords and Abstract

4 Choose your Tool for Linking

5 Enjoy your Links!