According to the team at MakoLab SA, the company has published a new ontology that supports precise descriptions of used cars. The Used Cars Ontology (UCO) was created in cooperation with Professor Martin Hepp (Hepp Research GmbH), creator of the GoodRelations ontology. The Used Cars Ontology “represents knowledge concerning used cars regardless of their manufacturer. The notions included in it cover properties which are significant in the used cars market and characterize the basic state of a given car, such as car modifications, damages, additions, information about the owner, as well as more detailed data, such as whether the owner of the car smoked cigarettes, what kinds of animals were transported in the car, etc.” Read more

“The idea of the Big S Semantic Web seems to have fallen off by the wayside in publishing as people are just trying to structure their data,” says Barbara McGlamery, taxonomist at Martha Stewart Living Omnimedia.

McGlamery, who will be presenting a case study comparing her experiences in two publishing houses that took opposite approaches to the semantic web at the SemTech conference in NYC this month, says that the path most publishers are on now “hardly seems like the same beast” as the one she formerly knew. A few years back, the focus was on RDF, OWL, full-blown ontologies and inferencing engines, whereas today “it’s schema.org and we’re using microdata, not even RDFa.”

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VIVO, a semantic information representation system that enables collaboration among scientists across all disciplines, has had a busy summer: The open source project to facilitate the advancement of research and discovery by integrating information about scholars, their activities, and outputs, gained a more permanent home in the DuraSpace Incubator, ensuring a way to continue activities after its NIH grant continuation year ran out. It saw the publication of VIVO: A Semantic Approach to Scholarly Networking and Discovery. And Northwestern University brought its researchers together in a single hub called Northwestern Scholars, an implementation of Elsevier’s SciVal Experts research networking tool (see our story here).

The future is looking pretty bright, too. “We are very interested in funding, research resources, scholarly works, scholars and data sets,” says Mike Conlon, primary investigator of the VIVO project. “As the world moves forward, these things are all inter-related, but that’s been very blurry, especially to organizations and institutions.” Funding agencies, for example, want to know what work was produced as a result of its grants to a major center. It no longer is just a question of who wrote a paper, but who funded it, what tools were behind it, and how was the data produced, and how all these things inter-relate in a scholarly data system.

“Connecting these things becomes the work of the future,” says Conlon.

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Barbara Starr recently discussed on Search Engine Land how search engines and social engines are incorporating semantic search. She writes, “The term ‘Semantic Search’ is certainly not new. However, it has taken on a new dimension and implications in both search and social engines today. In addition, it has had a strong impact on targeted semantic advertising. This special series of forthcoming articles on semantic search will take a look at the history behind the development of semantic technology and why it has now become so commercially viable and topical. It will also take a look at how the technology enables “answer engines,” rather than simple search engines, to improve the user experience.” Read more

Phys.org recently shared an interesting ontology case study in the field of botany, Ontologies as Integrative Tools for Plant Science. The article states, “Botany is plagued by the same problem as the rest of science and society: our ability to generate data quickly and cheaply is surpassing our ability to access and analyze it. In this age of big data, scientists facing too much information rely on computers to search large data sets for patterns that are beyond the capability of humans to recognize—but computers can only interpret data based on the strict set of rules in their programming.”

The article continues, “A new article in this month’s American Journal of Botany by Ramona Walls (New York Botanical Garden) and colleagues describes how scientists build ontologies such as the Plant Ontology (PO) and how these tools can transform plant science by facilitating new ways of gathering and exploring data. Read more

A fascinating project has been undertaken by the Partners of the Pan-Canadian Documentary Heritage Network (PCDHN): It’s a proof-of-concept showcase of using Linked Open Data visualizations for “Out of the Trenches.” This is a look at the First World War from the Canadian perspective: war songs, postcards, newspapers, photos, films, and these resources’ intersection with Canadian soldiers who fought in the war.

These digital resources from organizations such as McGill University, the Universities of Alberta, Calgary and Saskatchewan, and the Bibliothèque et Archives nationales du Québec have been linked through existing metadata provided in formats ranging from spreadsheets to MODS XML to RDF. Rather than reduce the metadata to a common subset, the approach was to maximize its use by moving to the “web of data” concept, so that the resources can be combined in different and unexpected ways, according to the proof-of-concept final report that was issued on the project.

The premise was to expose the metadata for these resources using RDF XML and existing published ontologies such as the Event Ontology, the Dublin Core Ontology and the Biographical Ontology, elements sets, vocabularies and resources like the Geonames geographical database to maximize discovery by the user community and contribute to the Semantic Web.

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Edamam wants to be the one place where all the food knowledge of the world is organized. That’s the goal of co-founder and CEO Victor Penev, who launched the site in April, and recently updated the several hundred major recipe sites in its knowledge base to also include some smaller blog sites that add additional variety.

Semantic technology is helping the company reach its goal. “A big problem is that data about food is very messy,” says Penev. “It’s hard to find something, what you find often contradicts other information of what is good for you and what the calories are. So we set out to solve that problem. We played around with different approaches but settled on using semantic technology.”

The confusion arises in part from the fact that recipe sites themselves usually just hire services to calculate nutritional data. But that may lead to mistakes when calculations aren’t undertaken with exactitude — substituting white cream for heavy cream nutritional details changes the whole profile of the recipe, he says.

So, what is that right semantic stuff? One piece of it is that, in conjunction with Ontotext, Edamam built a food ontology. An ontology can be the foundation for a lot of things, such as extracting the knowledge of the chemical composition of a particular recipe and thus inferring its flavor and texture. And Edamam means to grow its own to include various datasets such as chemical data (for flavor and texture), geolocation (for local and seasonal recipes), product data (for e-commerce). and more.

But initially, it’s taken the simple approach, with the core of the ontology focused around classifying ingredients, nutrients and food. “We have started with the simplest ontology and focused on the most common use case — mobile recipe search,” he says.

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Janna Hastings recently reported that ontologies have become an essential component of life science research. She writes, “Ontology has become the method of choice throughout most of biology and biomedicine for constructing and maintaining standardisations of the terminology used in database annotations.  This was the primary motivation for the development of the Gene Ontology, and remains until today a pressing and urgent requirement throughout computer-assisted science in many different fields.  This is thus the first application of bio-ontology in data-driven science.” Read more

Back in March, The Semantic Web Blog wrote an article about FIBO, the Financial Industry Business Ontology that’s on its way to being an Object Management Group series of standards. There, we explored its value as an open semantic standard that can be used by financial institutions and industry regulators, both to support conformance to federal regulatory reporting requirements and for internal business processes and risk analysis.

To continue the discussion about the operational value of FIBO, we recently spoke with key participants developing the standard: David Newman, Strategic Planning Manager, Vice President, Enterprise Architecture, Wells Fargo Bank, who is lead of the industry team collaborating on semantics OTC (over-the-counter) derivatives proof-of-concept, and Mike Atkin, managing director at the Enterprise Data Management (EDM) Council, where FIBO was born and is included as content of EDM’s Semantics Repository.

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How to help the bench biologist get value out of the wealth of life sciences Linked Data sets? Startup Metaome Science Informatics proposes to offer some help with its DistilBio semantic search and data integration technology, by streamlining the approach to posing user queries. The Distil in DistilBio stands for Data Integration using Semantic Technologies in the Life Sciences.

Metaome, which was founded by CEO Kalpana Krishnaswami and CTO Ramkumar Nandakumar as a bioinformatics services provider before transitioning to a product vendor, contains a few more than a dozen life sciences public data sets so far. Infomaticians in the life sciences space have the expertise to query such data across sets via SPARQL, but the front-line biologist isn’t necessarily an infomatician. So, DistilBio has created a query interface that makes it easier for them to ask large and complex questions in a simplified way across data sets while building a graph in the process.

“How does a user say what are the drugs used for Alzheimer’s disease and do have they have certain protein targets and are those protein targets implicated in other diseases?” says Krishnaswami. “To ask that in one shot right now is hard without working through a SPARQL endpoint using all the SPARQL syntax.”

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