DMCA
From Information to Knowledge: Harvesting Entities and Relationships from Web Sources
| Citations: | 26 - 7 self |
BibTeX
@MISC{Weikum_frominformation,
author = {Gerhard Weikum and Martin Theobald},
title = {From Information to Knowledge: Harvesting Entities and Relationships from Web Sources},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. Recent endeavors of this kind include DBpedia, EntityCube, KnowItAll, ReadTheWeb, and our own YAGO-NAGA project (and others). The goal is to automatically construct and maintain a comprehensive knowledge base of facts about named entities, their semantic classes, and their mutual relations as well as temporal contexts, with high precision and high recall. This tutorial discusses state-ofthe-art methods, research opportunities, and open challenges along this avenue of knowledge harvesting.
Keyphrases
harvesting entity web source state-ofthe-art method semantic class comprehensive knowledge base major trend natural-language web source research opportunity mutual relation recent endeavor knowledge-sharing community yago-naga project knowledge harvesting search engine high recall expressive semantic level temporal context high precision open challenge
