Reasoning is the task of deriving implicit facts from a set of given explicit facts. These facts can be expressed in OWL 2 ontologies and stored RDF triplestores. For example, the following fact: “a Student is a Person,” can be expressed in an ontology, while the fact: “Bob is a Student,” can be stored in a triplestore. A reasoner is a software application that is able to reason. For example, a reasoner is able to infer the following implicit fact: “Bob is a Person.”

Reasoning Tasks

Reasoning tasks considered in OWL 2 are: ontology consistency, class satisfiability, classification, instance checking, and conjunctive query answering.

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Image Courtesy: Flickr/Patrick Hoesly

The financial services industry is taking to semantic tech in an important way, and that’s in the form of the Financial Industry Business Ontology (FIBO), which aims to standardize the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and the legal obligations and process aspects of corporate actions. Attendees at SemTech Biz in San Francisco will get a deep dive on the how’s and why’s, at this session, while the FIBO Technology Summit invitation event will present an opportunity for working collaboratively to continue advancing the effort that has its roots in The Enterprise Data Management Council and communities of interests.

Leading that event will be Dennis E. Wisnosky, founder of Wizdom Systems, Inc. and former CTO and Chief Architect of the DoD Business Mission Area, who was recently named to provide technical strategy and operational guidance to help the Council finalize and implement FIBO standards, and David S. Newman, SVP & Strategic Planning Manager Enterprise Architecture at Wells Fargo, and Chair of the EDM Council’s Semantics Program. (Newman, with Enterprise Data Management Council Head of Semantics and Standards Mike Bennett, will also host the SemTech FIBO session.)  Speaking of the upcoming event, Wisnosky explains that a goal is to cast a wide net to find the new tech ideas and developments that both can bring benefits to FIBO in the short term and influence the longer-term research agenda to help the financial industry.

As FIBO stands now, in June the second draft of the FIBO Foundations ontology and the conceptual FIBO Business Entities ontology will be presented at a meeting of the Object Management Group in Berlin. By year’s end it is expected that the OMG will have ratified these as formal standards. “We are on the path to turn the corner from thinking of what FIBO will be to delivering it,” says Wisnosky. Read more

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IT leaders keeping an eye on Gartner’s top tech trends list know that early in March semantic technologies made the cut (see our original story here, and our follow-up with one of the authors of the Gartner report here). The big question for many enterprise IT pros, though, is what should they be doing with that knowledge – how can they start leveraging semantic technology to their own organizations’ benefit?

Help is on the way. Three experts in semantic web technologies and Linked Data weigh in with their advice on heading down that road:

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Semantic technologies have made it to Gartner’s list of the top technology trends that will impact information infrastructure this year.

The research firm yesterday released the list of nine trends that it says will play key roles in modernizing information management and in making the role of information governance increasingly important. Semantic technologies come in at No.3 on the list – right behind closely-tied-to trends Big Data and modern information infrastructure.

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OWL, the Web Ontology Language has been standardized by W3C as a powerful language to represent knowledge (i.e. ontologies) on the Web. OWL has two functionalities. The first functionality is to express knowledge in an unambiguous way. This is accomplished by representing knowledge as set of concepts within a particular domain and the relationship between these concepts. If we only take into account this functionality, then the goal is very similar to that of UML or Entity-Relationship diagrams. The second functionality is to be able to draw conclusions from the knowledge that has been expressed. In other words, be able to infer implicit knowledge from the explicit knowledge. We call this reasoning and this is what distinguishes OWL from UML or other modeling languages.

OWL evolved from several proposals and became a standard in 2004. This was subsequently extended in 2008 by a second standard version, OWL 2. With OWL, you have the possibility of expressing all kinds of knowledge. The basic building blocks of an ontology are concepts (a.k.a classes) and the relationships between the classes (a.k.a properties).  For example, if we were to create an ontology about a university, the classes would include Student, Professor, Courses while the properties would be isEnrolled, because a Student is enrolled in a Course, and isTaughtBy, because a Professor teaches a Course.

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Clinical studies aren’t what they used to be. In the past, the process was one-off: You conducted a study, gathered a lot of data, analyzed it, wrote a report, and submitted it to the authorities. But, says long-time Linked Data advocate Kerstin Forsberg, an information architect at AstraZeneca, that’s all changed in the last few years.

“A study is not a study on its own,” says Forsberg. Today, the goal is  to do meta-analysis across many studies, so parties ranging from  pharmaceuticals companies to contract research organizations to government authorities all are ‘customers’ of clinical data, so to speak. Data from various studies must be shared among all these parties. “It puts a new context around clinical trial data, that it must be easy to link data together, to link across several different studies,” she says.

The case is there to use modern information standards, like semantic web standards and Linked Data principles, to address this need. It’s why Forsberg is one of the individuals spearheading a volunteer effort to create RDF and OWL representations of the standards published by the Clinical Data Interchange Standards Consortium (CDISC) an international, non-profit organization that develops and supports global data standards for medical research.

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In recent blogs we’ve discussed where semantic technologies have gone in 2012, and a bit about where they will go this year (see here, here and here).

Here are some final thoughts from our panel of semantic web experts on what to expect to see as the New Year rings in:

John Breslin,lecturer at NUI Galway, researcher and unit leader at DERI, creator of SIOC, and co-founder of Technology Voice and StreamGlider

Broader deployment of the schema.org terms is likely. In the study by Muehlisen and Bizer in July this year, we saw Open Graph Protocol, DC, FOAF, RSS, SIOC and Creative Commons still topping the ranks of top semantic vocabularies being used. In 2013 and beyond, I expect to see schema.org jump to the top of that list.

Christine Connors, Chief Ontologist, Knowledgent:

I think we will see an uptick in the job market for semantic technologists in the enterprise; primarily in the Fortune 2000. I expect to see some M&A activity as well from systems providers and integrators who recognize the desire to have a semantic component in their product suite. (No, I have no direct knowledge; it is my hunch!)

We will see increased competition from data analytics vendors who try to add RDF, OWL or graphstores to their existing platforms. I anticipate saying, at the end of 2013, that many of these immature deployments will leave some project teams disappointed. The mature vendors will need to put resources into sales and business development, with the right partners for consulting and systems integration, to be ready to respond to calls for proposals and assistance.

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SKOS, which stands for Simple Knowledge Organization System, is a W3C standard, based on other Semantic Web standards (RDF and OWL), that provides a way to represent controlled vocabularies, taxonomies and thesauri. Specifically, SKOS itself is an OWL ontology and it can be written out in any RDF syntax.

Before we dive into SKOS, what is the difference between Controlled Vocabulary, Taxonomy and Thesaurus?

A controlled vocabulary is a list of terms which a community or organization has agreed upon. For example: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday are the days of the week.

A taxonomy is a controlled vocabulary organized in a hierarchy. For example, we can have the terms Computer, Tablet and Laptop and the concepts Tablet and Laptop are subclasses of Computer because a Tablet and Laptop are types of Computers.

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“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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There has always been a misconception between the relationship of RDF and XML. The main difference: XML is a syntax while RDF is a data model.

RDF has several syntaxes (Turtle, N3, etc) and XML is one of those (known as RDF/XML). Actually, RDF/XML is the only W3C standard syntax for RDF (Currently, there is Last Call on Turtle, a new W3C standard syntax for RDF). Therefore, comparing XML and RDF is like comparing apples with oranges. What can be compared is their data models. The RDF data model is a graph while the XML data model is a tree.

Comparing RDF with XML

Joshua Tauberer has an excellent comparison between RDF and XML, which I recommend. Two advantages of RDF are highlighted: flexibility of the data model and use of URIs as global unique identifiers.

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