Large-Scale Assessment of Deep Relational Machines
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A more common approach is to transform relational data into a propositional tabular form that can be used as an input to a neural network.
“A more common approach is to transform relational data into a propositional tabular form that can be used as an input to a neural network (Dash et al., 2018; Kaur et al., 2019, 2020).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Dash et al., 2018
Cited by
Sorted by citations
A review of some techniques for inclusion of domain-knowledge into deep neural networks
Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022) · Scientific Reports · doi:10.1038/s41598-021-04590-0 · Cited by 196 · see citing works
“Conceptually, there is no variable-sharing between two or more first-order logic features [25].”
BackgroundMethodSelf-citationInductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
“A more common approach is to transform relational data into a propositional tabular form that can be used as an input to a neural network (Dash et al., 2018; Kaur et al., 2019, 2020).”
Method
- Authors
- Dash, T., Srinivasan, A., Vig, L., Orhobor, O. I., & King, R. D.
- Affiliations
- Birla Institute of Technology and Science, Pilani - Goa Campus (IN)
- Tata Consultancy Services (India) (IN)
- University of Manchester (GB)
- Corresponding author
- Dash, Tirtharaj
- Srinivasan, Ashwin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 22-37
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2888684310
- Cited by
- 19
- References
- 0
- Citations by year
- 2025: 2 · 2023: 1 · 2022: 3 · 2021: 7 · 2020: 3 · 2019: 3
- FWCI
- 1.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- relational features, domain knowledge encoding, logic programs, inductive logic programming, benchmark dataset, biochemical data, regression analysis, classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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