Dominik Schweiger, Zlatko Trajanoski and Stephan Pabinger recently wrote, “Semantic Web has established itself as a framework for using and sharing data across applications and database boundaries. Here, we present a web-based platform for querying biological Semantic Web databases in a graphical way. Results: SPARQLGraph offers an intuitive drag &drop query builder, which converts the visual graph into a query and executes it on a public endpoint. The tool integrates several publicly available Semantic Web databases, including the databases of the just recently released EBI RDF platform. Furthermore, it provides several predefined template queries for answering biological questions. Users can easily create and save new query graphs, which can also be shared with other researchers.” Read more
WASHINGTON, D.C. – SYSTAP, LLC. today announced that Syapse, the leading provider of software for enabling precision medicine, has selected Bigdata® as its backend semantic database. Syapse, which launched the Precision Medicine Data Platform in 2011, will use the Bigdata® database as a key element of their semantic platform. The Syapse Precision Medicine Data Platform integrates medical data, omics data, and biomedical knowledge for use in the clinic. Syapse software is delivered as a cloud-based SaaS, enabling access from anywhere with an internet connection, regular software updates and new features, and online collaboration and delivery of results, with minimal IT resources required. Syapse applications comply with HIPAA/HITECH, and data in the Syapse platform are protected according to industry standards.
Syapse’s Precision Medicine Data Platform features a semantic layer that provides powerful data modeling, query, and integration functionality. According to Syapse CTO and Co-Founder, Tony Loeser, Ph.D., “We have adopted SYSTAP’s graph database, Bigdata®, as our RDF store. Bigdata’s exceptional scalability, query performance, and high-availability architecture make it an enterprise-class foundation for our semantic technology stack.”
There is no doubt about it: Schema.org is a big success. It has motivated hundreds of thousands of Web site owners to add structured data markup to their HTML templates and brought the idea of exchanging structured data over the WWW from the labs and prototypes to real business.
Unfortunately, the support for information about the sales and rental of vehicles, namely cars, motorbikes, trucks, boats, and bikes has been insufficient for quite a while. Besides two simple classes for http://schema.org/Vehicle and http://schema.org/Car with no additional properties, there was nothing in the vocabulary that would help marking up granular vehicle information in new or used car listing sites or car rental offers.
Recently, Mirek Sopek, Karol Szczepański and I have released a fully-fledged extension proposal for schema.org that fixes this shortcoming and paves the ground for much better automotive Web sites in the light of marketing with structured data.
This proposal builds on the following vehicle-related extensions for GoodRelations, the e-commerce model of schema.org:
- Vehicle Sales Ontology (VSO), http://purl.org/vso/ns
- Volkswagen Vehicles Ontology (VVO), http://purl.org/vvo/ns
- Used Cars Ontology (UCO), http://purl.org/uco/ns
It adds the core classes, properties and enumerated values for describing cars, trucks, busses, bikes, and boats and their features. For describing commercial aspects of related offers, http://schema.org/Offer already provides the necessary level of detail. Thus, our proposal does not add new elements for commercial features.
Daniel Newman of Forbes recently wrote his third and final article in a series on the future of marketing and how that future is interwoven with semantic search. Newman writes, “The internet is getting smarter and this growing intelligence and insights is populating a new kind of semantic web that is providing more than just the most relevant results for people searching, but also some key data to marketers that may just tell us about intent. For movie fans out there, you may remember the movie Minority Report. In this Tom Cruise feature film the star would go out and stop crimes before they would happen as intelligence reached a point where it could see a crime that was about to be committed. At the time the concept seemed pretty far fetched, but really this type of intelligence is very similar to how the semantic web may be able to tell you who may be your next big customer.” Read more
Peter Murray-Rust of OpenSource.com recently wrote, “Open is about sharing and collaboration. It’s the idea that ‘we’ is more powerful, more rewarding and fulfilling than ‘I’. I can’t promise jobs, but I do know that openis becoming very big. Governments and funders are pushing the open agenda, even though academics are generally uninterested or seriously self-interested. Some governments and some companies recognize the value of teams; academia and academics generally don’t. The false values of impact factor and the false values of academic publishing mean that open access is a poor reflection of open, or what you may recognize as the open source way.” Read more
Adrienne Lafrance of The Atlantic reports, “One of the tasks the human brain best performs is identifying patterns. We’re so hardwired this way, researchers have found, that we sometimes invent repetitions and groupings that aren’t there as a way to feel in control. Pattern recognition is, of course, a skill computers have, too. And machines can group data at scales and with speeds unlike anything a human brain might attempt. It’s what makes computers so powerful and so useful. And seeing the structural framework for patterns across vast systems of categorization can be enormously revealing, too.” Read more
A recent article in Medical Xpress reports, “Machine learning has been improved by Dr Thomas Wilhelm of the Institute of Food Research, which is strategically funded by the Biotechnology and Biological Sciences Research Council. Instead of developing one model from the training data, his technique involves developing hundreds of diverse models, and applying these to independent, unseen data, and seeing which models work best in their ability to predict outcomes. This avoids ‘overfitting’ of a model to a specific training data set. The new technique can be applied to many different situations, but Dr Wilhelm applied it to epigenetic data on cervical cancer.” Read more
NEW YORK, NEW YORK, Jul 31, 2014 — ADmantX, the next-generation contextual analysis and semantic data provider, today announced the completion of $2.4M financing. As demand for new audience data and semantic targeting increases, the investment will support the company’s strategy for commercial expansion and evolution of its product line.
Increasing reliance on data for brand protection, audience profiling and optimizing the match between ads and page content require solutions for targeting that are innovative and effective. Read more
Daniel Newman of Forbes recently wrote, “Businesses today are largely online and they have in droves taken their web presence from where it was a few years ago which was likely an “Online Brochure” to some type of second generation website that considers trends such as social media, content marketing, and of course search engine optimization. The reason we as business owners do all of this isn’t because we love technology (not all of us, at least), but rather because we know that people are doing more and more of their research about what they want to buy, and who from, online.” Read more