See the full article on Scoop.it – Data & Informatics
Quite a lot to digest here, though the overall sentiment is positive for development and innovation around open and linked data. Actual products as opposed to proofs, pilots and concepts.
There is also renewed optimism that the Semantic Web can deliver on its original vision, Semantic Web 2.0 (my term) utilising ‘cognition-as-as-service’ (CaaS), and building bridges between ‘Big Data’ and the Semantic Web in order to rurn unstructured chaos into higher level insights.
The following abstract caught my eye:
One less obvious problem is one of information retrieval. Keyword search is now fundamentally broken. The more information is out there, the worse keyword search performs. Advanced query systems like Facebook’s Graph Search or Wolfram Alpha are only marginally better than keyword search. Even conversation engines like Siri have a fundamental problem. No one knows what questions to ask. We need a web in which information (both questions and answers) finds you based on how your attention, emotions and thinking interconnects with the rest of the world.
Sounds good if a little utopian.
Overall, some useful insights in this piece.
Original source: semanticweb.com
I was pleased to attend a presentation on linked data at the BCS Data Management Specialist Group on Tuesday (26th July), given by Dave Reynolds, co-founder of Epimorphics Ltd, and one of the data experts I have frequently turned to for advice when scoping the requirements for the Knowledge Hub project. (Dave is a members of the Data & Apps Advisory Group for the Knowledge Hub).
The presentation included metadata management, e-Commerce uses, inference and information extraction, text mining, syntax (various flavours – RDF/XML, Turtle, RDfa), and knowledge representation through Ontologies (e.g. Web Ontology Language, OWL).
Dave explained a fairly complex topic (well, complex for those not yet fully immersed in modelling information solutions using linked data) in a simple but engaging style, using his slides to show examples of linked data constructs. Well worth a look for anyone who wants to get a deeper understanding of the topic (if nothing else, check out the strengths/weaknesses towards the end of the presentation).
The slides are available from SlideShare: Introduction to linked data, and a copy embedded below.