Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
DRETa: Extracting RDF from Wikitables [POSTER]
Search
Emir Muñoz
October 23, 2013
Research
0
59
DRETa: Extracting RDF from Wikitables [POSTER]
DRETa: Extracting RDF from Wikitables
Posters & Demos @ ISWC 2013
Emir Muñoz
October 23, 2013
Tweet
Share
More Decks by Emir Muñoz
See All by Emir Muñoz
Machine Learning Pipelines in Production - ML Galway Meetup
emunoz
0
68
Academic Writing: Hints and Tools
emunoz
0
150
Mining Cardinalities from Knowledge Bases
emunoz
0
220
Using Drug Similarities for Discovery of Possible Adverse Reactions
emunoz
0
140
A Hybrid Method for Rating Prediction Using Linked Data Features and Text Reviews
emunoz
0
210
On Learnability of Cardinality Constraints from RDF Data
emunoz
0
170
Minute Madness ESWC 2016
emunoz
0
100
Tensor Networks---a brief description
emunoz
0
100
A Linked Data-Based Decision Tree Classifier to Review Movies
emunoz
1
230
Other Decks in Research
See All in Research
一人称視点映像解析の最先端(MIRU2025 チュートリアル)
takumayagi
6
3.9k
アニメにおける宇宙猫ミームとその表現
yttrium173340
0
100
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation
satai
4
340
カスタマーサクセスの視点からAWS Summitの展示を考える~製品開発で活用できる勘所~
masakiokuda
2
210
不確実性下における目的と手段の統合的探索に向けた連続腕バンディットの応用 / iot70_gp_rff_mab
monochromegane
2
190
Google Agent Development Kit (ADK) 入門 🚀
mickey_kubo
2
2.2k
「どう育てるか」より「どう働きたいか」〜スクラムマスターの最初の一歩〜
hirakawa51
0
950
説明可能な機械学習と数理最適化
kelicht
0
220
【輪講資料】Moshi: a speech-text foundation model for real-time dialogue
hpprc
3
760
情報技術の社会実装に向けた応用と課題:ニュースメディアの事例から / appmech-jsce 2025
upura
0
230
SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images
satai
3
330
Mechanistic Interpretability:解釈可能性研究の新たな潮流
koshiro_aoki
1
480
Featured
See All Featured
BBQ
matthewcrist
89
9.8k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
31
2.6k
Side Projects
sachag
455
43k
Why Our Code Smells
bkeepers
PRO
340
57k
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
333
22k
The Pragmatic Product Professional
lauravandoore
36
7k
Why You Should Never Use an ORM
jnunemaker
PRO
59
9.6k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.2k
VelocityConf: Rendering Performance Case Studies
addyosmani
332
24k
Designing for humans not robots
tammielis
254
26k
Designing Experiences People Love
moore
142
24k
[RailsConf 2023] Rails as a piece of cake
palkan
57
5.9k
Transcript
Enabling Networked Knowledge ACKNOWLEDGEMENTS: This work was funded in part
by Science Foundation Ireland under Grant No. SFI/08/CE/I1380 (Lion-2). DRETA: EXTRACTING RDF FROM WIKITABLES Emir Muñoz, Aidan Hogan, Alessandra Mileo National University of Ireland, Galway MOTIVATION WIKITABLE SURVEY player http://dbpedia.org/resource/David_de_Gea http://dbpedia.org/resource/Rafael_Pereira_da_Silva_(footballer_born_1990) http://dbpedia.org/resource/Patrice_Evra …. http://dbpedia.org/resource/Fabio_Pereira_da_Silva http://dbpedia.org/resource/Tom_Cleverley http://dbpedia.org/resource/Darren_Fletcher PROPOSAL http://dbpedia.org/resource/Manchester_United_F.C. http://dbpedia.org/resource/England http://dbpedia.org/resource/Forward_(association_football) http://dbpedia.org/resource/Wayne_Rooney dbo:birthPlace dbp:currentclub dbp:position http://dbpedia.org/resource/Spain http://dbpedia.org/resource/Goalkeeper_(association_football) http://dbpedia.org/resource/David_de_Gea dbp:position http://dbpedia.org/resource/Brazil http://dbpedia.org/resource/Defender_(association_football) http://dbpedia.org/resource/Fabio_Pereira_da_Silva dbp:position … … (1) dbr:David_de_Gea dbo:birthPlace dbr:Spain . (2) dbr:Fabio_Pereira_de_Silva dbo:birthPlace dbr:Brazil . (3) dbr:Fabio_Pereira_de_Silva dbp:currentclub dbr:Manchester_United_F.C . SUGGESTED TRIPLES: SELECT ?player WHERE { ?player dbp:currentclub dbr:Manchester_United_F.C . } TABLE TAXONOMY: DISTRIBUTIONS: QUERY: RESULTS DEMO … http://emunoz.org/wikitables (1) EXTRACTED 34.9 MILLION UNIQUE & NOVEL TRIPLES FROM 1.14 MILLION WIKITABLES (8 MACHINES: 4GB RAM, 2.2 GHZ SINGLE CORE; 12 DAYS) (2) INITIAL EVALUATION: (MANUAL ANNOTATION; THREE JUDGES; 750 TRIPLES EACH) (3) MACHINE LEARNING CLASSIFIERS: (CONSENSUS GOLD STANDARD; VARIETY OF FEATURES) FROM 1.14 MILLION WIKITABLES: BAGGING DECISION TREES: SUPPORT VECTOR MACHINES: 1.14 MILLION WIKITABLES: 7.9 MILLION TRIPLES @81.5% PREC. 15.3 MILLION TRIPLES @72.4% PREC. … INCOMPLETE RESULTS!