A Semantic Sommelier as an Ontology-powered Mobile Social Application and a Pedagogical Tool

1. A Semantic Sommelier as an Ontology-powered Mobile Social Application and a Pedagogical Tool Deborah L. McGuinness and Evan W. Patton Tetherless World Constellation, Rensselaer Polytechnic Institute, Troy, NY 12180 Evolution News Exposure Technical Enhancements The Wine Ontology first debuted in press in a book chapter entitled “Living with CLASSIC: When and How to Use a KL-ONE-Like Language” in 1991. It was originally generated by the first author for a graduate school class. Since then it has seen translation into numerous languages and application in a number of Wine Agents, the most recent of which has received some press attention (see right-most column). On February 23rd, RPI released a press release entitled “A Semantic Sommelier: Wine Application Highlights the Power of Web 3.0”. There was a wide range of near immediate republications. Within 72 hours, articles appeared on sites covering a range of topics from food and wine to physics. Google currently indicates that there are roughly 1,490 web pages using the phrase “Semantic Sommelier”. We were interested in the wide interest so students collected a subset of the pages listed by Google and classified them according to the type of article and the overall category of the surrounding content. The first Wine Agent was developed at Stanford in 2003 by Eric Hsu under the direction of Dr. McGuinness. Users could choose a particular food or food description and obtain a matching wine description. The wine description was used as a query to a local wine knowledge base and was also used to query wine web services to obtain a particular wine suggestions. It used the JTP reasoner to determine pairings and used Inference Web to provide explanations for its recommendations along with sources for the content. The vast majority of pages were either teasers (a single paragraph or less of content with a pointer to a source article), complete reposts (either with or without attribution), or posts on content aggregator sites. There were also a small number of original blog posts that hypothesized about the future of these technologies and what could be done to improve them. The Mobile Wine Agent builds upon previous versions by incorporating contextual information such as GPS data and social network information (Facebook and Twitter) to provide a more robust, socially-aware recommendation system. It provides live classification of instance data that enables users to define new classes and instances instead of obtaining recommendations only from a fixed knowledge base and pre-selected web wine services. The primary focus of this implementation is on the ability to adjust the agent based on context. GPS, for example, allows us to identify the users location. This, combined with knowledge of local restaurants, their menus, and their wine lists, enables the agent to adjust what content is displayed to the user and even allows it to go out on the web of linked data and retrieve content not locally available. Similarly, there is an interface for specifying user preferences and making recommendations to other users of the agent, providing a set of tools for users to effect change in the behavior of the agent both for himself and potentially users in his social networks. James Michaelis developed a second Wine Agent in 2007 after Dr. McGuinness joined the Tetherless World. This version allowed users to contribute new recommendations via a wiki-based input system. Using the Pellet OWL reasoner, it would generate a class hierarchy of recommendations along with explanations for the wine-food pairings along with their provenance using the Proof Markup Language. Just over half of articles were posted on sites focused on general news, science, or technology (see right). Of the two areas that would be most affected by the success of the Wine Agent-the Food & Wine communities and the CS/AI/SW communities-only accounted for just under 21% of all citations. Dated articles were used to visualize the rate of publication across the web. Ideally, time information would have also been useful but few sites provided it. The column chart shows that most articles were published within the first 24 hours. We are still investigating the interest level although we hypothesize that the social and context connections were significant contributing factors. Acknowledgements This work was done by the Tetherless World Constellation, which has received support from Lockheed Martin, Fujitsu, LGS, and Microsoft Research. The work was partially supported by a NSF Graduate Research Fellowship to Evan Patton. We would also like to thank Daniel Souza, Philip Ng, Bharath Santosh, and Yu Chen for their help developing the agent and analyzing the news content.