Jennifer Zaino
SemanticWeb.com Contributor

Christian Halaschek-Wiener, Aditya Kalyanpur, and Jennifer Golbeck have good reason for getting excited about the future of the semantic web. They are actively, and have been actively, contributing to its development. (For more on how they got started, see Young Guns Driving Semantic Web (Part 1). Here’s a look at the work the three are doing.

Christian Halaschek-Wiener

Halaschek-Wiener, now CTO at a financial domain startup, has been recognized for work he has done on the semantic web and the financial services sector. For his dissertation, he developed a content dissemination framework founded upon the semantic web standard called OWL (Web Ontology Language) that leverages OWL’s reasoning capabilities for matching newly published information with users’ interests. The syndication framework provides many benefits over traditional approaches that use syntactic matching techniques (such as keyword or XML-based matching), including inferring additional publication matches for users’ interests via the inference capabilities (specifically description logic reasoning) provided by OWL, he says.

“There’s been talk of doing this more expressive syndication, and the hard problem I was trying to solve was how to make this more expressive syndication practical for high demand syndication domains,” such as in the financial services area, Halaschek-Wiener says. “There are tons of financial information publishers. But there are time constraints on how long it takes to process newly published information and integrate it into whatever other system is on the other end of line. Time is money from an analyst point of view, and so to process the information in a timely manner is of critical importance.”

For all its context advantages, OWL’s reasoning techniques introduce some overhead issues, which is problematic in a dynamic environment where there is a constant inflow of news stories and information about whether there is a match with respect to some user query. For example, an analyst may want to know any news that is related to a certain class of companies. Making the assumption that the news stories are encoded in OWL, whenever a new publication comes into the framework, the system Halaschek-Wiener envisioned would determine if there is a match for the new publication with the subscriber’s requirements.

Dow-Jones Newswires, a leading provider of global business news and information services, has an historical database of news feed articles going back about 20 years, all of them in XML with metadata tags. The company furnished him with its datasets and subscriptions of interest for their domains and clients, which allowed him to apply his framework to a high-demand syndication domain. This enabled him to perform a real-world assessment of the scalability of the framework in general, as well as the incremental OWL reasoning algorithms that he developed in his dissertation to make the framework practical.

“The results were promising, and we showed that, given some realistic ontologies and domain modeling, we could keep up with the news wires,” he says.

Aditya Kalyanpur

As a graduate student, Kalyanpur says his most interesting work was on the debugging and repair of RDF/OWL datasets. Prior to this work, he says, OWL reasoning tools would only tell users about errors in their data, with no explanation of how these came about or how to fix them. Given the complex nature of the logic underlying OWL, users found it very difficult to understand and debug these errors, he said. His work focused on building a set of OWL debugging tools that would explain the precise cause of ontology errors in a user-friendly manner, and suggest appropriate repair plans.

“Interestingly, the solutions developed are of a more general nature and can be used to explain any logical inference (not just an error) that follows from an RDF/OWL dataset,” he says. “Thus, for example, any OWL reasoning engine that does semantic query answering over an associated RDF data store can use the techniques developed to justify/explain an answer to the query, which can be very helpful to end users.”

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Jennifer Zaino
SemanticWeb.com Contributor

In the last few years, computer science has lost some of its luster as a career option for the next generation. But there’s reason for excitement again, as semantic web standards pave the way for new technologies, services, and business opportunities.

Just ask 27-year old Christian Halaschek-Wiener. He recently received his PhD under the direction of one of the seminal figures in the semantic web community, Dr. James A. Hendler, from the Computer Science Department of the University of Maryland. Now, Halaschek-Wiener is a chief technical officer at a startup in the San Francisco Bay Area within the financial domain.

At the University of Maryland, his dissertation work was essentially an application of some of the reasoning capabilities that languages such as OWL can provide, especially around content dissemination over the web — in colloquial terms, news aggregation on steroids. Halascheck-Wiener says he really enjoyed the time he spent in academia, but by transitioning to the private sector he could make sure that he could continue investigating some of the more practical aspects of applying some of these technologies to real world problems.

Another reason: “Right now in the more commercial space, it’s quite an active and newly emerging area, so the timing seemed quite good to move out,” he says. “There’s some common names that we are seeing in the media that are very vocal and active in the space, like Radar Networks and Metaweb. I think we will continue to see companies such as these coming up with new and innovative ways to apply the technology.”

Having new and interesting problems to solve in a constantly evolving area is a good reason for getting up to go to work each day, he says. “That’s what’s unique about this field.”

Another young tech, Aditya Kalyanpur, who started graduate studies at the University of Maryland in 2001, has also moved to the commercial world, working at IBM TJ Watson Research Center “on an exciting project that aims to solve one of the main barriers to the adoption of Semantic Web technologies — scalability of ontology reasoning on the Web. When he started his graduate studies, Kalyanpur says, there was quite a bit of buzz forming around the semantic web. Hendler, his advisor, had just written about the semantic web vision with Tim Berners Lee and Ora Lasilla in the Scientific American; some early W3C standards activity was taking place around semantic web languages such as RDF and OWL; and numerous academic research projects were taking off in this area.

One such project at the University of Maryland that caught his eye was SHOE (Simple HTML Ontology Extension) which was about adding simple tags to HTML to describe web page content better using ontological entities — categories, relations, and rules. A relatively simple Knowledge Representation language, it demonstrated the value of adding a semantic layer over HTML to do tasks such as search better.

“The research area is clearly relevant, presenting opportunities for high-impact work given the pervasiveness and popularity of the Web, while being challenging, due to the numerous social as well technical issues that come from applying KR principles to the Web,” Kalyanpur says.

He’s excited by the huge potential for developing mainstream semantic web applications thanks to advances in AI algorithms and the push toward establishing a large community around shared knowledge resources. For example, the W3C-backed “Linking Open Data” project aims to consolidate the rich knowledge-bases around open, accessible and structured repositories such as DBpedia, GeoNames, MusicBrainz and their ilk into a giant “data cloud,” whose datasets are all exposed in RDF and interlinked to related ones, he says.

“This is a very promising initiative, since as this community grows, we should expect to see more mainstream semantic web applications and services leveraging this wealth of structured information,” he says. “Already, we have seen a few interesting prototypical applications come out of the Semantic Web Challenge that make use of third-party datasets, such as an ontology-backed multimedia search engine, and a general-purpose reviewing and rating site, Revyu, which uses RDF beneath the covers. On the horizon are applications such as PowerSet and Twine which combine NLP, machine-learning, and semantic web technologies to do smarter search and analytics.”

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Jennifer Zaino
SemanticWeb.com Contributor

Auction giant eBay is taking to the semantic web for helping to document its systems as part of its grid computing initiative in its data center. Semanticweb.com recently discussed why and how with Neel Sundaresan, Director of eBay Research Labs.

Semanticweb.com: What is the main purpose of using semantic technologies at eBay?

Sundaresan: The first and the main purpose of using semantic technologies is documentation. We know that a well-documented system is a well-maintained system. As we have entities like operating systems, databases, servers, storage systems, and instances of these, we want to be able to describe two things — what are their properties and how these entities are related to each other. When you want to create new instances you want to know conceptually and structurally where they fit. Hence, a well-defined object hierarchy becomes important.

When you want to modify or delete instances you further want to know how that affects entities they are related to. In a complex large-scale system, defining these properties, inheritances, and relationships help us better manage, troubleshoot and scale the systems. Semantic technologies provide the right fit.

RDF provides the underlying framework for semantic description of our systems. OWL is a derivative of RDF with a stronger machine interpretability, which is important when you want software to interpret your rules to provide interfaces or take actions. We are both a consumer of these technologies and a key player in driving the Grid standards (OGF). [The OGF is the international community dedicated to accelerating grid computing adoption by providing an open forum for grid innovation and developing open standards for grid software interoperability; standards such as RDF can be used to extend current grids into semantic grids in which information and services are given well-defined meaning.]

Any well-managed and documented system should be using some sort of semantic technology. [But] people don’t always wait for standards to come around to help them solve their problems. That is why many of these tend to be one-off implementations. The vision of semantic web included RDF as a syntax and schema framework for a metadata description language for the web. RDF itself was preceded by other work for non-web environments. RDF is just a framework and is not the only framework. Some [organizations] went with RDF while others did not. Specific instances of RDF have to be created for individual domains.

For instance, almost a decade ago, while some of us were using it to describe the Web, other standards were being formed. OSD (Open Software Description) comes to mind — it was a language for describing contents and dependencies of software packages. It was used to deliver different types of software to diverse client platforms.

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