Zobrazeno 1 - 10
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pro vyhledávání: '"Sack, Harald"'
Autor:
Biswas, Russa, Kaffee, Lucie-Aimée, Cochez, Michael, Dumbrava, Stefania, Jendal, Theis E., Lissandrini, Matteo, Lopez, Vanessa, Mencía, Eneldo Loza, Paulheim, Heiko, Sack, Harald, Vakaj, Edlira Kalemi, de Melo, Gerard
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 1, Iss 1, Pp 4:1-4:32 (2023)
While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph comp
Externí odkaz:
https://doaj.org/article/5ecb460fc50c4c999be30b70ac3bb957
This paper presents NFDIcore 2.0, an ontology compliant with the Basic Formal Ontology (BFO) designed to represent the diverse research communities of the National Research Data Infrastructure (NFDI) in Germany. NFDIcore ensures the interoperability
Externí odkaz:
http://arxiv.org/abs/2410.01821
The NFDI4DataScience (NFDI4DS) project aims to enhance the accessibility and interoperability of research data within Data Science (DS) and Artificial Intelligence (AI) by connecting digital artifacts and ensuring they adhere to FAIR (Findable, Acces
Externí odkaz:
http://arxiv.org/abs/2408.08698
Ontologies are widely used in materials science to describe experiments, processes, material properties, and experimental and computational workflows. Numerous online platforms are available for accessing and sharing ontologies in Materials Science a
Externí odkaz:
http://arxiv.org/abs/2408.06034
Ontology and knowledge graph matching systems are evaluated annually by the Ontology Alignment Evaluation Initiative (OAEI). More and more systems use machine learning-based approaches, including large language models. The training and validation dat
Externí odkaz:
http://arxiv.org/abs/2404.18542
Autor:
Santini, Cristian, Posthumus, Etienne, Tan, Mary Ann, Bruns, Oleksandra, Tietz, Tabea, Sack, Harald
Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack
Externí odkaz:
http://arxiv.org/abs/2306.16529
Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these methods are inductive LP models which are capable of learning represe
Externí odkaz:
http://arxiv.org/abs/2211.11407