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.
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.”
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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.
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.”