Large language models and knowledge graphs: Opportunities and challenges
Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …
Knowledge graphs for the life sciences: Recent developments, challenges and opportunities
The term life sciences refers to the disciplines that study living organisms and life processes,
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
Contextual semantic embeddings for ontology subsumption prediction
Automating ontology construction and curation is an important but challenging task in
knowledge engineering and artificial intelligence. Prediction by machine learning …
knowledge engineering and artificial intelligence. Prediction by machine learning …
Olala: Ontology matching with large language models
S Hertling, H Paulheim - Proceedings of the 12th Knowledge Capture …, 2023 - dl.acm.org
Ontology (and more generally: Knowledge Graph) Matching is a challenging task where
information in natural language is one of the most important signals to process. With the rise …
information in natural language is one of the most important signals to process. With the rise …
Results of the ontology alignment evaluation initiative 2020
The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching
systems on precisely defined test cases. These test cases can be based on ontologies of …
systems on precisely defined test cases. These test cases can be based on ontologies of …
DeepOnto: A Python package for ontology engineering with deep learning
Applying deep learning techniques, particularly language models (LMs), in ontology
engineering has raised widespread attention. However, deep learning frameworks like …
engineering has raised widespread attention. However, deep learning frameworks like …
LogMap family participation in the OAEI 2017
E Jiménez-Ruiz, B Grau, V Cross - CEUR Workshop Proceedings, 2017 - ora.ox.ac.uk
We present the participation of LogMap and its variants in the OAEI 2017 campaign. The
LogMap project started in January 2011 with the objective of developing a scalable and …
LogMap project started in January 2011 with the objective of developing a scalable and …
Exploring the in-context learning ability of large language model for biomedical concept linking
The biomedical field relies heavily on concept linking in various areas such as literature
mining, graph alignment, information retrieval, question-answering, data, and knowledge …
mining, graph alignment, information retrieval, question-answering, data, and knowledge …
Reveal the unknown: Out-of-knowledge-base mention discovery with entity linking
Discovering entity mentions that are out of a Knowledge Base (KB) from texts plays a critical
role in KB maintenance, but has not yet been fully explored. The current methods are mostly …
role in KB maintenance, but has not yet been fully explored. The current methods are mostly …
Llms4om: Matching ontologies with large language models
Ontology Matching (OM), is a critical task in knowledge integration, where aligning
heterogeneous ontologies facilitates data interoperability and knowledge sharing …
heterogeneous ontologies facilitates data interoperability and knowledge sharing …