AGDISTIS - Graph-Based Disambiguation of Named Entities using Linked Data (Best Research Paper nominee)
published: Dec. 19, 2014, recorded: October 2014, views: 3970
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Re- source Description Framework) from these websites. One of the key steps towards extracting RDF from text is the disambiguation of named entities. While several approaches aim to tackle this problem, they still achieve poor accuracy. We address this drawback by presenting AGDIS- TIS, a novel knowledge-base-agnostic approach for named entity disambiguation. Our approach combines the Hypertext-Induced Topic Search (HITS) algorithm with label expansion strategies and string similarity measures. Based on this combination, AGDISTIS can eﬃciently detect the correct URIs for a given set of named entities within an input text. We evaluate our approach on eight diﬀerent datasets against state-of-the- art named entity disambiguation frameworks. Our results indicate that we outperform the state-of-the-art approach by up to 29% F-measure.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !