Industry

For the past decade or so, Knowledge Graphs have been sneaking into our daily lives, be it through voice assistants (such as Alexa, Siri or Google Assistant), intuitive search results or even personalized shopping experiences through online store recommenders.

Making sense of data using semantics, open domain knowledge and standards to reach for interoperability within the public sector
 

Machine Learning technologies have been used throughout the community and industry successfully for several years now. In this session we bring together technology experts and lawyers from different areas to discuss with us their digital success, experience, learnings and most especially vision for the future.

To make adequate decisions, businesses have to combine their databases - their own view of the world - with non-proprietary data. However, combining diverse data from multiple sources is a complex task.

A story about elephant and poachers. About rangers fighting wildlife crime. And about the role of semantics in all this.
A story also, about how high-tech companies are helping rangers to turn wild spaces into safe havens.
 

The Legal domain is complex in terms of underlying texts and language, amount of data and frequency of changes. Therefore, the process of designing a legal research solution becomes a more and more challenging task.

I will introduce the concept of 'Semantic AI', which is based on a fusion of NLP, machine learning (ML) and semantic knowledge graphs. We will take a look at two concrete use cases: How to improve ML-based classifiers for legal documents with semantic enrichment.

Smart text mining is a critical and meanwhile established tool in the tool-box of modern law firms. Out of the box mining requires a lot of training of statistic AI algorithms. A semantic layer in law firms’ text mining approach could unlock a huge step forward but requires more work on the user interface. Here is a wish-list to the semantic AI community from a law firm’s perspective.