Signals are emerging pieces of information relevant to a given context and offer potential for strategic advantage in a multitude of domains. However, sorting the signal from noise on large textual data is a very tedious process for humans. We introduce a scalable approach that extracts signals f...

The main objective of Drugs4Covid is to create resources, following the principles of Open Science, that facilitate the extraction of knowledge from scientific literature related to the Coronavirus. These resources can be used by scientific communities that carry out research in relation to SARS-...

Despite the excitement about Large Language Models (LLM), these models suffer from hallucinations problems, e.g., generating factually incorrect text. These problems restrict the development of production-ready applications. This talk will highlight the importance of combining Knowledge Graphs wi...

Significant portions of the data generated in enterprises are unstructured and text-based. This can span the entire product lifecycle, from early research to post-launch analysis. A major challenge for companies is managing these vast amounts of text data and extracting hidden and valuable inform...

Many industries store vast amounts of information as natural language. Current methods for composing this text into knowledge graphs parse a small set of relations from within a larger document. The author's specific diction is approximated by the vocabulary of the model. In domains where precise...

Natural language search over a knowledge graph presents unique challenges as the entities of a knowledge graph differ in structure compared to traditional documents. In this talk, we discuss methods of implementing natural language search over entity space within a knowledge graph using such tech...