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Presentation of "Facet Annotation Using Reference Knowledge Bases" at the WWW2018 Research Track, i.e., The Web Conference 2018, April 26th, Lyon, France.
ABSTRACT: Faceted interfaces are omnipresent on the web to support data exploration and filtering. A facet is a triple: a domain (e.g., Book), a property (e.g., author,lanдuaдe), and a set of property values (e.g., { Austen, Beauvoir, Coelho, Dostoevsky, Eco, Kerouac, Suskind, ... },{ French, English, German, Italian, Portuguese, Russian, ... } ). Given a property (e.g., language), selecting one or more of its values (Enдlish and Italian) returns the domain entities (of type Book) that match the given values (the books that are written in English or Italian). To implement faceted interfaces in a way that is scalable to very large datasets, it is necessary to automate facet extraction. Prior work associates a facet domain with a set of homogeneous values, but does not annotate the facet property. In this paper, we annotate the facet property with a predicate from a reference Knowledge Base (KB) so as to maximize the semantic similarity between the property and the predicate. We define semantic similarity in terms of three new metrics: specificity, coverage, and frequency. Our experimental evaluation uses the DBpedia and YAGO K Bs and shows that for the facet annotation problem, we obtain better results than a state-of-the-art approach for the annotation of web tables as modified to annotate a set of values.
For more info about our work you can check out the websites of our labs:
INSID&S Lab (UNIMIB): http://inside.disco.unimib.it/
ADVIS Lab (UIC): https://www.cs.uic.edu/bin/view/Advis/WebHome
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