ptlm_wsid (this link opens in a new window) by semantic-web-company (this link opens in a new window)
Usage of pre-trained language model to induce and disambiguate entity senses
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How to correctly link entities from text to your Knowledge Graph using Word Sense Induction and Disambiguation.
A quicker and more robust Word Sense Induction and Disambiguation approach for Entity Linking.
Enabling reliable disambiguation without the need to induce at all, even with incomplete sense inventories.
Type: Conference Paper
Venue: SEMANTiCS 2024
Authors: Artem Revenko, Anna Breit, Salma Mahmoud, Mark Szabo, Tomas Knap
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Venue: SEMANTiCS 2022
Authors: Victor Mireles, Artem Revenko, Anna Breit, Peter Bourgonje, Julián Moreno Schneider and Maria Khvalchik
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Venue: SEPLN 2022
Authors: Artem Revenko and Patricia Martín-Chozas
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Venue: SEMANTiCS
Authors: Artem Revenko, Anna Breit, Victor Mireles, Julian Moreno-Schneider, Christian Sageder, and Sotirios Karampatakis
View publicationType: Workshop Paper
Venue: DeepOntoNLP @ESWC
Authors: Patricia Martín-Chozas, Artem Revenko
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Venue: LegalAI @IEEE BigData
Authors: Anna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou
View publicationType: Workshop Paper
Authors: Peter Bourgonje, Anna Breit, Maria Khvalchik, Victor Mireles, Julian Moreno-Schneider, Artem Revenko, Georg Rehm
View publicationGeoCrow project aims to help military forces by conducting disaster relief and emergency operations by providing relevant information about future operational areas through semantically enriched, categorised data, and their visualisation in a virtual reality environment.
Visit Project WebsiteThe main objective of Lynx is to create an ecosystem of smart cloud services to better manage compliance, based on a legal knowledge graph (LKG) which integrates and links heterogeneous compliance data sources including legislation, case law, standards and other private contracts.
Visit Project WebsiteThe FFG-funded OBARIS project aims to advance the status quo in the area of auditable semantic artificial intelligence systems (SAISs), by investigating both conceptual aspects of these systems as well as a technology stack that facilitates transposing these system types into concrete settings.
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