Abstract Entity Linking (EL) empowers Natural Language Processing applications by linking
relevant mentions found in raw textual data to precise information about what they …

We provide a comprehensive survey of the research literature that applies Information
Extraction techniques in a Semantic Web setting. Works in the intersection of these two …

The rapid generation of large amounts of information about the coronavirus SARS-CoV-2
and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview …

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph.
Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent …

A large number of machine translation approaches have recently been developed to
facilitate the fluid migration of content across languages. However, the literature suggests …

This research paper proposes an approach called Lingua Franca that improves machine
translation quality by utilizing information from a knowledge graph to translate named …

As advances in science and technology, crisis, and increased competition impact labor
markets, reskilling and upskilling programs emerged to mitigate their effects. Since …

While neural networks have led to substantial progress in machine translation, their success
depends heavily on large amounts of training data. However, parallel training corpora are …

Abstract The Entity Linking (EL) task identifies entity mentions in a text corpus and
associates them with corresponding entities in a given knowledge base. While traditional EL …

Abstract Knowledge Graph Question Answering (KGQA) systems are often based on
machine learning algorithms, requiring thousands of question-answer pairs as training …