News

Finding new needles in content haystacks: Vocabulary maintenance with AI!

Introduction Structured vocabularies (thesauri, taxonomies...) play an important role in many applications where complex, large and volatile information needs to be organized and made accessible. A fine example is the famous MeSH thesaurus that facilitates search and access on Medical topics. Enriching scientific content with MeSH terms allows to guarantee that content on a specific … Continue reading Finding new needles in content haystacks: Vocabulary maintenance with AI!

Fraunhofer SCAI and Kairntech succeed with AI-powered Relation Extraction for Indication-Specific Knowledge Graph assembly

A Knowledge Graph for psychiatric disorders SANKT AUGUSTIN.   The Fraunhofer Institute for Algorithms and Scientific Computing SCAI and the software company Kairntech (Grenoble) have created a knowledge graph for psychiatric disorders, especially psychoses. Knowledge graphs allow unstructured texts to be represented in a structured, comparable format. They visualize cause-and-effect models to help medical professionals … Continue reading Fraunhofer SCAI and Kairntech succeed with AI-powered Relation Extraction for Indication-Specific Knowledge Graph assembly

Random check is dead, long live exhaustivity

Random check vs Exhaustivity In many professions controls are carried out on a random basis. It is a generally accepted practice that an auditor reviewing agreements or an analyst screening litterature proceeds with random checks simply because it is humanly impossible and much too costly to process each individual document. At the same time society … Continue reading Random check is dead, long live exhaustivity

Jumpstart your Machine Learning efforts by importing structured knowledge

Introduction Machine Learning approaches in NLP have been shown to be able to solve a wide range of tasks after being trained from scratch on an appropriate training corpus. While this is impressive, it often does not correspond to the demand in many real-world scenarios. Often relevant prior knowledge exists – in the case of … Continue reading Jumpstart your Machine Learning efforts by importing structured knowledge

Make the long road to augmented applications shorter by “do-it-yourself NLP”

Introduction Natural language processing (NLP) techniques have made tremendous progress during the last couple of years, thanks notably to algorithms such as Neural Networks which have surpassed traditional approaches with rules-based systems. Examples include Machine Translation (DeepL, Google Translate…) or Speech To Text (Siri…). Information Extraction, a subcategory of NLP, is also highly impacted.  What … Continue reading Make the long road to augmented applications shorter by “do-it-yourself NLP”

Extracting Numbers from Text

Introduction Named Entity Recognition relates to the extraction of sequences of words from within a document. The technique is mostly used to extract names of people, organisations or places, which are therefore the most typical named entities. However, the term named entity recognition does not capture very well the importance of a fragment of text, … Continue reading Extracting Numbers from Text

Bootstrap World Knowledge to extend Business Vocabulary and enhance Knowledge Graphs

Introduction Information extraction tends to target two situations:  Extract entities from an existing vocabulary, or Create an extraction model from scratch when there is no existing vocabulary. However, sometimes the situation is a mixture of the two extremes: an incomplete business vocabulary exists but needs to be completed with relevant additional entities of the same type. … Continue reading Bootstrap World Knowledge to extend Business Vocabulary and enhance Knowledge Graphs