Sieve: linked data quality assessment and fusion
By Pablo N. Mendes, Hannes Mühleisen, Christian Bizer. EDBT/ICDT Workshops, 2012
312 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/dcc359c1045c2d093c33a57c0d06278c4b340e12
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jens Lehmann
- Sören Auer
- Sebastian Hellmann
- Dimitris Kontokostas
- Amrapali Zaveri
- Christian Bizer
- Heiko Paulheim
- Tom Heath
In Information Systems
- Jens Lehmann
- Sören Auer
- Sebastian Hellmann
- Andrea Maurino
- Amrapali Zaveri
- Gerhard Weikum
- Fabian M. Suchanek
- Christian Bizer
In Management Science and Operations Research
Citations in the corpus
- Quality assessment for Linked Data: A Survey (2015, methodology): “…[45] – CN2: extensional conciseness measured by: ∗ no. of unique instances of a dataset total number of instances representations in the dataset [45], ∗ 1 − total no. of instances that violate the uniqueness rule total no. of relevant instances [18,36,41] – CN3: detection of unambiguous…”
- Test-driven evaluation of linked data quality (2014, methodology): “Approaches can be broadly classified into (i) automated (e.g. [12]), (ii) semi-automated (e.g. [9]) or (iii) manual (e.g. [2, 22]) methodologies.”
- Knowledge Graphs (2020, unknown): “[348] distinguish intensional conciseness (schema level), which refers to the case when the data does not contain redundant schema elements (properties, classes, shapes, etc.”