This file is in the following communities:
This file is not currently in any collections.
Accurate Fact Harvesting from Natural Language Text in Wikipedia with Lector. Open Access
- Author or creator
- Additional contributors
- Type of item
This item is a resource in the University of Alberta Libraries' Dataverse Network. Access this item in Dataverse by clicking on the DOI link.Many approaches have been introduced recently to auto- matically create or augment Knowledge Graphs (KGs) with facts extracted from online resources such as Wikipedia, par- ticularly from its structured components like the infoboxes. Although these structures are valuable, they represent only a fraction of the actual information expressed in the articles. In this work, we quantify the volume of highly accurate facts that can be harvested with high precision from Wikipedia text articles using information extraction techniques boot- strapped from the entities and relations already in a KG. Our experimental evaluation, performed on facts about en- tities in the domain of people, reveals we can augment such Freebase relations by more than 10%, with facts whose ac- curacy are over 95%. Moreover, that vast majority of these facts are missing from the infoboxes, YAGO and DBpedia.
- Date created
- License information
- Citation for previous publication
- Link to related item
- Date Uploaded
- Date Modified
- Audit Status
- Audits have not yet been run on this file.
- not yet characterized