Semantic Web Dog Food

Presented at: The Sixth International Language Resources and Evaluation Conference (LREC2008)

by Adrian Popescu, Gregory Grefenstette

People use the Internet to find a wide variety of images. Existing image search engines do not understand the pictures they return. The introduction of semantic layers in information retrieval frameworks may enhance the quality of the results compared to existing systems. One important challenge in the field is to develop architectures that fit the requirements of real-life applications, like the Internet search engines. In this paper, we describe Olive, an image retrieval application that exploits a large scale conceptual hierarchy (extracted from WordNet) to automatically reformulate user queries, search for associated images and present results in an interactive and structured fashion. When searching a concept in the hierarchy, Olive reformulates the query using its deepest subtypes in WordNet. On the answers page, the system displays a selection of related classes and proposes a content based retrieval functionality among the pictures sharing the same linguistic label. In order to validate our approach, we run to series of tests to assess the performances of the application and report the results here. First, two precision evaluations over a panel of concepts from different domains are realized and second, a user test is designed so as to assess the interaction with the system.

Keywords: Information Extraction, Information Retrieval, Multimedia annotation and processing, Tools, systems, applications, Linguistics

Resource URI on the dog food server: http://data.semanticweb.org/conference/lrec/2008/papers/84

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