Early Steps Towards Web Scale Information Extraction with LODIE

SPRING 2015 55 Extracting information from a gigantic data source such as the web has been considered a major research challenge, and over the years many different approaches (Etzioni et al. 2004; Banko et al. 2007; Carlson et al. 2010; Freedman and Ramshaw 2011; Nakashole, Theobald, and Weikum 2011) have been proposed. Nevertheless, the current state of the art has mainly addressed tasks for which resources for training are available (for example, the TAP ontology in the paper by Etzioni and colleagues [2004]) or use generic patterns to extract generic facts (for example, Banko et al. [2007]; OpenCalais.com). The limited availability of resources for training has so far prevented the study of the generalized use of large-scale resources to port to specific user information needs. The linked open data information-extraction (LODIE) project focuses on the study of IE models and algorithms able to perform efficient user-centered web-scale learning by exploiting linked open data (LOD). In this article we will highlight the initial steps of the LODIE project, focusing on a specific IE task, wrapper induction (WI), which consists of automatically learning wrappers for uniform web pages, that is, pages from one website, usually generated with the same script and all describing the same type of entity. We show results on the WI task, exploiting linked data obtained from DBpedia as learning material. Linked data is a recom-