FactCheck is a framework for detecting and resolving conflicting data on the Web. It establishes an entire fact comparison process that consists of data acquisition, data comparison, the presentation of comparison results, and comprehensive analysis functions. FactCheck is a leading research topic of our research group and bears challenges in many aspects. To enhance data acquisition, your task is to link the extracted information to existing knowledge bases.
Named Entity Recognition (NER) and Entity Linking (EL) are critical components of natural language processing (NLP) applications, enabling machines to identify and link entities (e.g., people, places, organizations) in text to structured knowledge bases. However, the vast array of available entity linking APIs—such as DBpedia Spotlight, WAT, and Stanford NLP—often yield inconsistent results due to differences in their underlying algorithms and datasets. This inconsistency poses a challenge for developers and researchers seeking reliable and unified entity linking solutions.
This project aims to address this challenge by creating a web service that integrates multiple entity linking tools, allowing users to configure and combine them flexibly. The service will be showcased through an intuitive web application, enabling users to link named entities in web pages or text using customizable configurations.
Technologies: Web Applications; Web Services; Python; Javascript; Hugging Face Transformers Library; SpaCy Library; PyTorch; Schema.org
Tags: FactCheck; Python; Entity Linking; Natural Language Processing; Web Application; Web Service; Knowledge-base; Hofer