py-entity-linking 0.0.1

A library for lightweight entity linking

Lightweight entity linking solution.

Please consider citing our works if you use code from this repository.

Main dependencies

• python>=3.10
• numpy==1.26.4
• SPARQLWrapper==2.0.0
• sentence_transformers==3.1.1
• aiohttp==3.9.5
• openai==1.54.2
• beautifulsoup4==4.12.2
• fake-useragent==1.5.1

Example & Usage

from linking import main

# Your API token which can be found here (https://github.com/marketplace/models/azure-openai/gpt-4o)
api_token = "YOUR_API_TOKEN"

main.EL(api_token=api_token, sentence="We used PCA and FA for our experiments.", mention="PCA", single="No", combination="No", disambiguation="No", embedding_model="Lajavaness/bilingual-embedding-large")

Wikipedia: https://en.wikipedia.org/wiki/Principal_component_analysis

Wikidata: https://www.wikidata.org/wiki/Q2873

DBpedia: http://dbpedia.org/resource/Principal_component_analysis

Execution Time: 00:00:18

Parameters

api_token: Your API token from [here] (https://github.com/marketplace/models/azure-openai/gpt-4o). (Required) sentence: An english text. (Required) mention: The mention you want to perform the linking, the mention should be from inside the provided sentence. (Required) single: Usually used for difficult mentions, it searches each word of the mention individually, (deafult="No"), (Values: "Yes", "No"). (Optional) combination: Usually used for difficult mentions, it makes combinations for each word of the mention, (deafult="No"), (Values: "Yes", "No"). (Optional) embedding_model: A [sentence-transformers] (https://sbert.net/) model to perform text similarity, (deafault="Lajavaness/bilingual-embedding-large"), (Values: str of the name of any sentence-transformers model). (Optional)

Licence

This library is licensed under the CC-BY-NC 4.0 license.