Industry

Clinical decision support system application are a class of application to support physicians in their choice in treating patients. Cancer treatment is one of the area where Cdss applications can help physicians due to strict recommendations and the need for deciding if patients are eligible to enter clinical trial.

In Poland a pilot study conducted at the Marie-Curie Oncology Center in Warsaw (MCMCC) revealed that medical errors lead to decrease of patient survival at about 20%. The key point of our prototype was to educate and/or directly help medical practitioners in applying recommended treatment. 

Our application, called Clinical decision support system (Cdss) for GIST cancer treatment, has been developed in collaboration with the MCMCC to help physicians treat GIST cancer and is currently used experimentally in this center. Our application has been formalized using semantic web standards from the application structure to the patient and clinical recommendations that are shown. The application is using fast reasoning to decide what is the next step in the treatment and to add recommendations to the form that is shown. This kind of technologies have already been applied to such applications but never to this extent. The Cdss application that we have implemented is a practical implementation of the semantic form paradigm that we are working on in our company: a way to formalize forms by using semantic technologies to enhance productivity and data consistency.

Based on the technological concepts of cyber-physical systems, the Internet of Things and the Internet of Services, the vision of smart factories is summarized as “Industrie 4.0”. The goal: a smart factory that works adaptable and resource-efficient, integrating customers and partners into the business and value-added processes. Cyber-physical systems combine software technical components with mechanical and electronic parts.

Semantic technologies are seen as a key to the realization of such systems semantic technologies. They are able to integrate all product and production process relevant data and build a layer of connected information within one single model.
However, technical documentation for machinery and equipment actually still describes traditionally closed technical systems with clearly defined functions and components. The documentation for the manufactured products is also based on a fixed, predetermined product. Instead, documentation in the Age of Industrie 4.0 has to use flexible and topic oriented approaches to cover the adaptive and individualized products and services they are describing. Furthermore they also must support applications such as the operation and maintenance of the parts.

Building a common and controlled vocabulary that, enables personalization, searchability of localized content, data governance and standardization.

How a software integrator can ease the editorial process, optimize production and deliver outstanding user experience by utilising technologies like MarkLogic and semantics.

In the era of Smart Data and the explosion of data volume of all kind, organizations seek for leveraging such data - being it patent information, research literature social media data etc. - for competitive advantage and to help achieving their strategic aims. The process of search, filtering and categorization of large data sets go typically far beyond simple keyword search. Semantic technologies paired with machine learning approaches from artifical intelligence are a promising approach to support more fine-granular analysis of data.

The European Patent Office and Averbis recently went into collaboration for the pre-classification of incoming patent applications (use case 1) and re-classification of existing classification schemes (use case 2). In this cooperation, various services are provided with the aim of automatically assigning patent applications to the right departments and automatically allocating existing patents with new CPC codes. The solution is based on complex linguistic and semantic analyses, as well as statistically-based machine learning processes. Up to 250.000 incoming patents shall be classified per year and categorized in up to 1.500 categories. In this talk, we want to present both use cases together with some technical background about the applied language technologies.
 

MON, a subsidiary of Müller Medien has developed a framework of semantic representations comprising web services for a semantic web crawler, which can be used for auto classification and clustering the content of web sites, as well as web services for retrieving keyword clusters based on a system of facets and several thesauri for the relationships of branches. There are several implementations of the framework – from the automatic enrichment of SEO landing pages, the enhancement of search experience for white and yellow pages online and improving search facilities of web portals to tools for sales reps. The presentation will focus on one of the sales tools.

The Content model for Elsevier Optimized Learning Suite is an extensible framework to allow for authoring, storage and delivery of content assets that are used for highly interactive, personalized learning experiences. The content standards are based on W3C XML, HTML5, RDF, RDFa, JSON (Javascript Object Notation) and the extended JSON-LD (JSON for linking data) standards. An API supports the full workflow of content structuring, authoring, learning orchestration and deployment of learning objects into a product framework. 

The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, Allotrope Foundation is developing a comprehensive and innovative framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its life cycle. In this talk we describe how laboratory data and semantic metadata descriptions are brought together to ease the management of a vast amount of data that underpins almost every aspect of drug discovery and development.

We strongly believe that semantic technologies and open data (ie. in the form of open data thesauri and ontologies) are key factors to connect information and knowledge in the climate sector. Using open data to increase consistency in language and allow links between related documents (across languages and platforms) provides those who are affected by climate change as well as decision makers in the field of sustainable development access to accurate and timely knowledge on climate related issues.

Interactive session: See a demo and experience first-hand our Knowledgecafe!

As a European public administration, the General Secretariat of the EU Council needs to provide information to EU member states efficiently and effectively. This requires a knowledge-based administration. Equally, the data that we make available is addressed to citizens, researchers, journalists and others and should be available in a reusable format. Thus we embarked on a pilot project to make the votes of the EU Council available as a first open data set. This project helps greatly in developing a strongly semantics-based approach to internal and external information provision.