[NOTE: This guest post is by Peter Haase, Lead Architect for Research and Development, fluid Operations.]

Industry engineers waste a significant amount of time searching for data that they require for their core tasks. When informed about potential problems, diagnosis engineers at Siemens Energy Services, an integrated business unit which runs service centers for power plants, need to access several terabytes of time-stamped sensor data and several gigabytes of event data, including both raw and processed data. These engineers have to respond to about 1,000 service requests per center per year, and end up spending 80% of their time on data gathering alone. What makes this problem even worse is that their data grows at a rate of 30 gigabytes per day. Similarly, at Statoil Exploration, geology and geographic experts spend between 30 and 70% of their time looking for and assessing the quality of some 1,000 terabytes of relational data using diverse schemata and spread over 2,000 tables and multiple individual databases [1]. In such scenarios, it may take several days to formulate the queries that satisfy the information needs of the experts, typically involving the assistance of experienced IT experts who have been working with the database schemata for years.

Siemens and Statoil Exploration are hardly the only companies faced with time-wasting Big Data issues, but the root of these issues is not simply the “big” aspect of their data. The real challenge is finding a way to efficiently and effectively mine data for value and insight, regardless of its volume.

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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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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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Dennis Wisnosky is on-board to lead the standards implementation process for FIBO, the Financial Industry Business Ontology that is a joint effort of The Enterprise Data Management Council in conjunction with the Object Management Group.

The data management standards can be used by financial institutions and industry regulators to support conformance to federal regulatory reporting requirements and for internal business processes and risk analysis. Wisnosky, who previously was Chief Technical Officer and Chief Architect, Business Mission Area, U.S. Department of Defense, has spearheaded the U.S. DoD’s use of semantic technology across systems to meet the goal of having an “executable, integrated, consumable, solution architecture.” (See story here).

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The Internet of Things is coming, but it needs a semantic backbone to flourish. With some 25 billion devices expected to be connected to the Internet by 2015 and 50 billion by 2020, providing interoperability among the things on the IoT “is one of the most fundamental requirements to support object addressing, tracking, and discovery as well as information representation, storage, and exchange.” So write the authors of Semantics for the Internet of Things: Early Progress and Back to the Future, Payam Barnaghi and Wei Wang, Centre for Communication Systems Research, University of Surrey, Guildford, UK and Cory Henson, Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing.

Applying semantic technologies to IoT, however, has several research challenges, the authors note, pointing out that IoT and using semantics in IoT is still in its early days. Being in on the ground floor of this movement is undeniably exciting to the research community, including people such as Konstantinos Kotis, Senior Research Scientist at University of the Aegean, and IT Manager in the regional division of the Samos and Ikaria islands at North Aegean Regional Administration Authority.

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Courtesy: Flickr/zoetnet

As we close out 2012, we’ve asked some semantic tech experts to give us their take on the year that was. Was Big Data a boon for the semantic web, or is the opportunity to capitalize on the connection still pending? Is structured data on the web not just the future but the present? What sector is taking a strong lead in the semantic web space?

We begin with Part 1, with our experts listed in alphabetical order:

John Breslin, lecturer at NUI Galway, researcher and unit leader at DERI, creator of SIOC, and co-founder of Technology Voice and StreamGlider:
I think the schema.org initiative really gaining community support and a broader range of terms has been fantastic. It’s been great to see an easily understandable set of terms for describing the objects in web pages, but also leveraging the experience of work like GoodRelations rather than ignoring what has gone before. It’s also been encouraging to see the growth of Drupal 7 (which produces RDFa data) in the government sector: Estimates are that 24 percent of .gov CMS sites are now powered by Drupal.

Martin Böhringer, CEO & Co-Founder Hojoki:

For us it was very important to see Jena, our Semantic Web framework, becoming an Apache top-level project in April 2012. We see a lot of development pace in this project recently and see a chance to build an open source Semantic Web foundation which can handle cutting-edge requirements.

Still disappointing is the missing link between Semantic Web and the “cool” technologies and buzzwords. From what we see Semantic Web gives answers to some of the industry’s most challenging problems, but it still doesn’t seem to really find its place in relation to the cloud or big data (Hadoop).

Christine Connors, Chief Ontologist, Knowledgent:

One trend that I have seen is increased interest in the broader spectrum of semantic technologies in the enterprise. Graph stores, NoSQL, schema-less and more flexible systems, ontologies (& ontologists!) and integration with legacy systems. I believe the Big Data movement has had a positive impact on this field. We are hearing more and more about “Big Data Analytics” from our clients, partners and friends. The analytical power brought to bear by the semantic technology stack is sparking curiosity – what is it really? How can these models help me mitigate risk, more accurately predict outcomes, identify hidden intellectual assets, and streamline business processes? Real questions, tough questions: fun challenges!

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Edamam, which has built a food ontology for its food knowledge site (which The Semantic Web Blog initially covered here), is adding an iPad version of its app to its existing iPhone and Android versions. The company also did a full relaunch of its web site to optimize the experience for desktop users, as well, with improved browsing and search.

Originally, the web site app mirrored the mobile versions. But, says co-founder and CEO Victor Penev, “We realized that people wanted to be able to access recipes and search on the desktop, and they should have a holistic experience from anywhere.” While the company had been more focused on the mobile arena, Penev says building traffic for the website is going to be a priority too. Among the capabilities users should see in the near future are functions like one that will let people save recipes on their iPhone or Android mobile devices and then access them on their iPads or desktops, or vice verse.

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One of the exciting things about being a semantic technologist is the opportunity to be in on the ground floor of things as companies revamp, revise, and renew their infrastructures for the Web 3.0 world.

That’s the position that Keith DeWeese finds himself in. DeWeese recently moved from The Tribune Company, where he led efforts in applying semantic technology to the publisher’s content (see story here), to Ascend Learning, a company that provides technology-based education products with a focus on the healthcare sector.

There, as principal content architect he is again championing the power of semantic technology for online content. “What’s cool is that Ascend is in a state of redefining what it does, how it works, its whole platform,” DeWeese says. Ascend wants to be able to take people from the beginning stages of their career, when they’re learning the basics, and work with them throughout their life, so that as they progress in their careers and become more knowledgeable about their profession or specialization and work toward different exams, it’s got the tools to engage with them at that part of their lifecycle.

“It’s really great because there’s an openness and willingness to try different approaches to making content available to end users.”

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Dave Mccomb of Semantic Arts recently discussed how inspiration plays a part in designing ontologies. He writes, “It seems that there are three ways that ontologies are or can be related to applications. They are: (1) Inspiration. (2) Transformation. (3) Extension. To put this conversation in context, let’s go back to the ‘tic tac toe’ board [above]. What it is attempting to convey is that there are levels of abstraction and differences in perspective that we should consider when we are modeling. An application is in the lower middle cell. Data models are in the middle square. Ontologies could be anywhere. An ontology is a formal way of representing a model. And so we could have an ontology that describes an application, an ontology of a logical model, even ontologies of data or meta meta data.” Read more

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