Kategorie: Data Science

Der TIB Terminology Service – Terminologien und Ontologien unterstützen ein FAIRes Forschungsdatenmanagement

Um die wissenschaftliche Verwendung etablierter Ontologien und Terminologien zu unterstützen hat die TIB in Zusammenarbeit mit der NFDI-Initiative den TIB Terminology Service (TS) eingerichtet: eine zentrale Anlaufstelle für den Zugang zu Terminologien aus verschiedenen Bereichen wie Architektur, Chemie, Informatik, Mathematik und Physik.

The 2nd ORKG Curation Grant Program – Stories of Machine-Actionable Research Contributions

In 2022, TIB awarded its ORKG Curation Grants for the second time. 10 researchers from several disciplines made continuous entries in the ORKG in their field of research and contributed significant research issues to the ORKG. In this blog post, we introduce two of the them and show how they have worked with the ORKG and how their research has benefited from the ORKG.

Language Discourse in the context of Natural Language Processing – A Quick Look

Discourse takes various modalities, structures, and mediums. Among the commonly experienced mediums are face-to-face chats, telephone conversations, television news broadcasts, radio news, talk shows, lectures, books, and scientific articles. Intriguingly, each of these forms of discourse follow a logical structural nuance depending on the medium. The advancement of the digital age including social media communication has further led to expansion of logical discourse structures. These include blog-posts, emails, websites, review sites of products, hotels, restaurants or movies, and, finally, social media streams such as Twitter, Facebook, Reddit, #slack channels, Q&A portals such as Quora or Stack Overflow, etc., with a growing list as new mediums of communication over the World Wide Web are invented.
Large and varied streams of natural discourse only mean an even larger body of research questions remains to be explored!

NLP Should Go Beyond Commonsense Knowledge

NLP technology is all-pervasive for commonsense knowledge. There are many causes for this. Most of the internet and its data is about commonsense knowledge and world events, so NLP technology is developed over the data domain that is most easily available. But what about the scholarly domain with its rapidly growing body of knowledge produced worldwide? These are those specialized domains of knowledge in Science, Technology, Engineering, and Mathematics (STEM), all of which open up countless doors for NLP.

The opportunities are endless!