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Knowledge Graph enhanced Named Entity Recognition

In this area of research, we are investigating approaches to address the Named Entity Recognition (NER) task as well as how Knowledge Graphs can be used to improve upon and circumvent the shortcomings of existing models.
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Repositories and Demonstrators

Entity Recognition Demo

A web-demo showcasing the entity recognition capabilities of the PoolParty Extractor
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Videos, Slides and Posters

Lynx Services Platform (LynxSP) – Named Entity Recognition

May, 2021

Project Demo: Legal Knowledge Graph for Multilingual Compliance Services

Related Publications

Extracting Key Elements from Legal Permits: Software Architecture for a Real World Use Case

Type: Workshop Paper

Venue: LegalAI @IEEE BigData  

Authors: Anna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou

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Training NER Models: Knowledge Graphs in the Loop

Type: Poster

Venue: ESWC 2020  

Authors: Sotirios Karampatakis, Alexis Dimitriadis, Artem Revenko, Christian Blaschke

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Related Projects

Projects 1

LYNX: Legal Knowledge Graph for Smart Compliance Services

finished
Dec 1, 2017 Mar 31, 2021

The 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.

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OBARIS: Ontology-Based Artificial Intelligence in Environmental Sector

finished
Feb 1, 2020 Oct 31, 2022

The 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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