London, UK – May 8, 2013: Today, Ontology Systems, the semantic search company for structured enterprise application data, announce their nomination as finalist in the Big Data and Analytics category for the Pipeline COMENT Innovation Awards 2013.

The recognition comes as Ontology is being increasingly adopted by CSPs and enterprise across other industries, such as financial services, for its innovative uses of semantic search across large, complex data estates as a faster, more cost-effective, more resilient and more accurate alternative to traditional data integration approaches. Read more

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

What’s on the menu for semantic technology this week? Two vendors in the foodie field are offering up some new treats.

From Nara, whose neural networking technology is behind a service to help users better personalize and curate their restaurant dining experiences (see how in our story here), comes a new feature that should make picking a restaurant for a group dinner an easier affair. It combines users’ “digital DNA” – the sum of what it learns of what each one likes and doesn’t like regarding dining venues – to serve up restaurant choices that should appeal to the entire group across its range of preferences.

“It’s a really fun way to start getting [the service] into social,” says Nara founder and CEO Tom Copeman.

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Ontology Systems, the semantic search company for structured enterprise application data, announce Vodacom SA , the leading South African mobile service provider, has deployed Ontology to enhance their service assurance operations.

Ontology provides Vodacom with a comprehensive Service Assurance solution for Change Management and Fault Management operations. The solution provides a correlated view of Vodacom’s transport network (SDH and MPLS) topology in relation to Vodacom’s RAN network and Vodacom Business information in Siebel CRM that: Read more

We continue our discussion from yesterday of what enterprise IT will learn to love about semantic technologies at the upcoming SemTechBiz conference (the story began here):

Another Score For Data Agility

Looked at from the data warehouse point of view by Thomas Kelly, Practice Director, Enterprise Information Management, for Cognizant Technology Solutions, semantic technology makes it possible to apply Agile development practices to the data warehouse itself. “You can start modeling, work with data, generate analytics and then start tuning based on what you learn,” says Kelly, who will be discussing semantic technology for the data warehousing practitioner at this session. Several semantic technology-based practices can be applied that support iterative, evolutionary improvements with little or no impact to data loading and analytics functions that were built before the refinements were made, he says.

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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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Inventorying and managing cultural heritage data turns out to be a pretty complicated undertaking. The construction of a famous site may have lasted across different time periods, and its present location may span multiple districts. Buildings may be associated not only with famous architects but also with well-known residents. Or structures may have been constructed atop pre-existing entities.

Helping sort it all out is the work of The Arches Project, collaboration between the Getty Conservation Institute (GCI) and World Monuments Fund (WMF). The Arches effort grew out of GCI’s and WMF’s work to develop MEGA-Jordan, a purpose-built geographic information system (GIS) to inventory and manage archaeology sites at a national level for that country. But for this more generic and open-source take at accommodating any country, region or other institution worldwide responsible for the protection of immovable cultural heritage, the focus expanded from the geo-spatial to the semantic.

“We became very familiar with the CIDOC Conceptual Reference Model ontology,” says Alison Dalgity, who manages the Arches project on GCI’s side. The CRM provides definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation. “We realized we needed something like that. Now, the GIS piece is only part of this – it’s nice to know where something is, but all the other relationships – the who, how, what and when and so on – have to be represented, too.”

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Oversight Systems is in the business of Big Data analytics. Come June, it also will be in the business of having its technology serve as a platform behind third-party business intelligence and analytics applications on-demand – including its ontology approach for integrating data from disparate enterprise systems.

The company currently provides packaged solutions that let front-line employees involved in processes such as procure-to-pay or order-to-cash conduct continuous transaction analysis for insights into transactions that violate business rules, so that the business can take action to close gaps and assure compliance to operational and regulatory requirements. The ontology it’s developed over the years, which includes proprietary semantic and relationship information and infers some additional information, is there to help with the acquisition and preparation of data.

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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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The Semantic Web Blog earlier this month covered the news that Ontology Systems is updating its Ontology semantic search platform to Version 4.0, which was previewed last week at Mobile World Congress in Barcelona. Following that, we had an opportunity to catch up with CTO and co-founder Leo Zancani to learn more about the upcoming version of the platform that features new search capabilities under the Project Rothko code name banner, as well as the just-released Version 3.7, and how the telco and financial sectors its technology is focused on can leverage it.

The company’s Ontology platform connects its Ontology Intelligence 360 and Ontology Integrity Manager solutions. The former builds dependency models of business entities by looking at data in existing enterprise systems, and the latter is a data integrity solution that measures and monitors in an ongoing way the data alignment among various different systems that talk about the same thing. Those two products, Zancani explains, “depend on quite finely modeled data, so there are quite strict semantic models inside them. Data that is taken from existing systems populates those strict models, and customers are interested in then using the data in those models to drive business processes,” he says. Due in Version 4.0 that should debut in April, “Rothko adds the capability on the side that says, that’s great, but there also is value in the data you don’t want to or need to or can’t afford to model right now, and you can access that with a much more direct search capability.”

What’s the call for this two-tiered search approach in the verticals Ontology Systems is focused on? Take the telco sector, where the company founders have a long history. The industry, says Zancani, is in crisis now, as vendors like Apple and Google eat its lunch, and as the fallout from major consolidation among telco players makes traditional data integration economically untenable.

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