Industry

In recent years there is a rapid growth of unstructured text content and multimedia documents, which includes audios, videos and images in web as well as in the enterprise and these large volumes of data is not very useful without effective methods for content analysis and retrieval.

Linklaters is one of the world’s leading global law firms. The firm has a wealth of high value information held within our systems however due to the nature of these systems it is not always easy to leverage this value. Our goal was to improve decision making across the firm by transforming access to and ability to query data.

Fact and entity extraction from unstructured, natural language texts is already a very challenging problem. But even if a system can identify persons, organisations or other entities, it is very likely that an essential piece of information is missing:

Today’s Internet of Things (IoT) suffers from a lack of interoperability across platforms. As a consequence, semantic models for the IoT have recently emerged, both to describe domain knowledge and generic knowledge conceptualizing the interplay between IoT devices, services and real-world features.

In the past years, standardization in IoT has largely focused at the technical communication level, leading to a large number of different solutions based on various standards and protocols, with limited attention to the common semantics contained in the message data structures exchanged at the technical level.

Knowledge graph visualization is a big challenge but at the same time a huge success factor for a project. Often, graph visualizations are used but they do not cover all use cases, especially in an industry project. Thus, designing user interfaces for an information system based on semantic models takes a lot of time and effort.

This presentation showcases FREME, a framework for multilingual and semantic enrichment of digital content, in two industry domains. FREME deploys several industry and community standards from the area of data and language processing, to ease the creation and adaptation of enrichment applications.

This presentation aims to describe the adoption process of semantic web public data standards in Slovak Republic focusing on real use case scenarios. The standardization process started in 2013 under the Ministry of Finance where the main goal was to use URI for public resources. Since then a lot of work was done to enable usability of public linked data.

Document categorization and indexing is often either purely manually rule-based or  purely driven by statistics. In real-world scenarios the short comings of both approaches, respectively, prove problematic: Rule-based approaches require a lot of resources and insight. Statistical approaches require considerable training data which my not be available.

The massive amount of content being produced inside and outside the newsroom needs to be organized and curated to meet the evolving demands of the audience. Throwing news online without context and analysis simply doesn’t work when the focus for digital news is on interactivity, engagement and community.