DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference
By Feng Niu, Ce Zhang, Christopher R'e, Jude W. Shavlik. 2012
214 citations recorded by the source corpus.
Subfields: Information Retrieval & Search, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Retrieval & Search
- Li Fei-Fei
- Ranjay Krishna
- Theodoros Rekatsinas
- Heng Ji
- Mihai Surdeanu
- Hector Garcia-Molina
- Li Dong
- Michael S. Bernstein
In Natural Language Processing
- Mirella Lapata
- Mohit Bansal
- Li Dong
- Heng Ji
- Jianpeng Cheng
- Graham Neubig
- Luke S. Zettlemoyer
- Furu Wei
Citations in the corpus
- Long Short-Term Memory-Networks for Machine Reading (2016, background): “…to fact and relation extraction (Etzioni et al., 2011; Fader et al., 2011), knowledge base population and construction (Ji and Grishman, 2011; Niu et al., 2012; Carlson et al., 2010), ontology learning (Poon and Domingos, 2010b), textual entailment (Dagan et al., 2005), and the creation of…”
- Ground: A Data Context Service (2017, background): “Another is entity extraction and resolution from data, and the broader category of knowledge-base construction; examples citations here include DeepDive [27] and YAGO [33].”
- SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, methodology): “rm probabilistic inference to nd the most likely assignment to variables V o . Any factor graph framework could be used for this task. We perform this learning and inference in the DeepDive framework [1, 27, 39]. 4. ESTIMATING SOURCE ACCURACY We now consider different data fusion settings with respe”
- Overview of the English Slot Filling Track at the TAC2014 Knowledge Base Population Evaluation (2014, methodology): “Notably, the highest score this year was obtained using DeepDive4 (Niu et al., 2012), an IE framework based on DS that is a first participant in the SF evaluation.”
- Image retrieval using scene graphs (2015, methodology): “…use of such a description for image retrieval, we would need to resolve co-references in the text [53, 30, 39], perform relationship extraction to convert the unstructured text into structured tuples [47], and ground the entities of the tuples into regions of the image described by the text [33].”