Video Forum

Business success depends heavily on the ability to make effective use of data. The first step towards this data-driven culture is data access, but many organizations have data silos that hinder this effort.

While thousands of ontologies exist on the web, a unified system for handling online ontologies – in particular with respect to discovery, versioning, quality-control, mappings – has not yet surfaced and users of ontologies struggle with many challenges which can be reduced to the divide between stability and evolution.

Since its inception in 2007, DBpedia has been constantly releasing open data in RDF, extracted from various Wikimedia projects using a complex software system called the DBpedia Information Extraction Framework (DIEF).
For the past 12 years, the software has received a plethora of extensions by the community and produced releases with over 14.4 billion triples in 2016.

A key source of revenue for the media and entertainment domain is ad targeting: serving advertisements to a select set of visitors based on various captured visitor traits.

Semantic technologies offer significant potential for improving data search applications. Ongoing work thrives to equip data catalogs with new semantic search features to supplement existing keyword search and browsing capabilities. In particular within the social sciences, searching and reusing data is essential to foster efficient research.

In the field of domestic robotics, having a rich representation of knowledge offers flexibility to the robotic platform. It is important for the robotic platform to have capabilities that use semantic knowledge about how household objects are related to each other and with respect to a human action.

Semantic data integration from heterogeneous, distributed data silos enables Digital Humanities research and application development employing a larger, mutually enriched and interlinked knowledge graph. However, data integration is challenging, involving aligning the data models and reconciling the concepts and named entities, such as persons and places.

The recent deployments of semantic web tools and the expansion of available linked datasets have given users the opportunity of building increasingly complex applications. These emerging use cases often require queries containing mathematical formulas such as euclidean distances or unit conversions. Currently, the latest SPARQL standard (version 1.1) only embeds basic math operators.

While Open Data (OD) publishers are spur in providing data as Linked Open Data (LOD) to boost innovation and knowledge creation, the complexity of RDF querying languages, such as SPARQL, threatens their exploitation. We aim to help lay users (by focusing on OD experts) in querying and exploiting LOD by taking advantage of our target users' expertise in table manipulation and chart creation.