Industry

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.

The European Patent Office (EPO) provides patent protection for inventions in up to 40 European countries on the basis of one single application. The EPO also publishes these patents and make them available to the public in a variety of formats: via online tools, web services or bulk data.

Enterprise Knowledge is developing a semantic hub for an international development bank that provides content recommendations for the intranet, external website, and as an agent for meeting requests.  Learn how we designed this cutting edge solution and the challenges we faced in implementing it.

When approaching semantic content solutions, it’s often tempting to focus on technologies and platforms. This session looks at the interpersonal and business side of optimising content models and use of semantic structures inline (semantic XML) and centrally stored (ontology management).

SANSA is the first open source project that allows out of the box horizontally scalable analytics for large knowledge graphs. The talk will cover the main features of SANSA introducing its different layers namely, RDF, Query, Inference and Machine Learning. The talk also covers a large-scale Etherum blockchain use case at Alethio, a spinoff company of Consensys. 

AI Knowledge Graphs (AI KGs) are realizations of Linked Enterprise Data, creating valuable business insights by bringing together semantic technologies and enterprise data infrastructures. These Knowledge Graphs go beyond data integration: the semantics underlying these next generation AI applications provide machines with the advantage of understanding the meaning of large data volumes. 

Today’s market is flooded with Natural Language Processing (NLP) tools that allow for an easy extraction of sentiment information from raw text. Whereas these could be a good start to explore the kind of richness that sentiment analysis can bring to the table, much more is needed in order to do this at a level where real actionable insights come out of the raw data.