Popularity-guided top- k extraction of entity attributes
Matthew J. Solomon, Cong Yu, Luis Gravano · 2010
Recent progress in information extraction technology has enabled a vast array of applications that rely on structured data that is embedded in natural-language text. In particular, the extraction of concepts from the Web---with their desired attributes---is important to provide applications with rich, structured access to information. In this paper, we focus on an important family of concepts, namely, entities (e.g., people or organizations) and their attributes, and study how to efficiently and effectively extract them from Web-accessible text documents. Unfortunately, information extraction over the Web is challenging for both quality and efficiency reasons. Regarding quality, many sources on the Web contain misleading or invalid information; furthermore, extraction systems often return incorrect data. Regarding efficiency, information extraction is a time-consuming process, often involving expensive text-processing steps. We present a top-k extraction processing approach that addresses both the quality and efficiency challenges: for each entity and attribute of interest, we return the top-k values of the attribute for the entity according to a scoring function for extracted attribute values. This scoring function weighs the extraction confidence from individual documents, as well as the "importance" of the documents where the information originates. We define the document importance in terms of entity-specific document "popularity" statistics from a major search engine. Overall, our top-k extraction processing approach manages to identify the top attribute values for the entities of interest efficiently, as we demonstrate with a large-scale experimental evaluation over real-life data.