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Cited by 196Neural Networks and ApplicationsDomain Adaptation and Few-Shot LearningModel Reduction and Neural Networks

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A review of some techniques for inclusion of domain-knowledge into deep neural networks

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Dash, Tirtharaj, Chitlangia, Sharad, Ahuja, Aditya, Srinivasan, Ashwin·2022·Scientific Reports
DOI 10.1038/s41598-021-04590-0PMC PMC8776800
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Abstract

We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

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Figures

Figure 1 An example of AI for Science. The human-in-the-loop is a biologist. The biologist conducts experiments in a biological system, obtains experimental observations. The biologist then extracts data that can be used to construct machine learning model(s). Additionally, the machine learning system has access to domain knowledge that can be obtained from the biologist. The machine learning system then conveys its explanations to the biologist.
Figure 1 An example of AI for Science. The human-in-the-loop is a biologist. The biologist conducts experiments in a biological system, obtains experimental observations. The biologist then extracts data that can be used to construct machine learning model(s). Additionally, the machine learning system has access to domain knowledge that can be obtained from the biologist. The machine learning system then conveys its explanations to the biologist.CC BY · PubMed Central
Figure 2 The plots from6 showing gains in predictive accuracy of (a) multilayer perceptron (MLP), and (b) graph neural network (GNN) with the inclusion of domain-knowledge. The domain knowledge inclusion method in (a) is a simple technique known as ‘propositionalisation’ 7; and, the method in (b) is a general technique of incorporating domain-knowledge using bottom-graph construction. The results shown are over 70 datasets. No importance to be given to the line joining two points; this is done for visualisation purpose only.
Figure 2 The plots from6 showing gains in predictive accuracy of (a) multilayer perceptron (MLP), and (b) graph neural network (GNN) with the inclusion of domain-knowledge. The domain knowledge inclusion method in (a) is a simple technique known as ‘propositionalisation’ 7; and, the method in (b) is a general technique of incorporating domain-knowledge using bottom-graph construction. The results shown are over 70 datasets. No importance to be given to the line joining two points; this is done for visualisation purpose only.CC BY · PubMed Central
Figure 3 Informal descriptions of (a) logical; and (b) numerical constraints.
Figure 3 Informal descriptions of (a) logical; and (b) numerical constraints.CC BY · PubMed Central
Figure 4 Construction of a deep model M from data (D) using a learner (L). We use π to denote the structure (organisation of various layers, their interconnections etc.) and θ to denote the parameters (synaptic weights) of the deep network. L denotes the loss function (for example, cross-entropy loss in case of classification).
Figure 4 Construction of a deep model M from data (D) using a learner (L). We use π to denote the structure (organisation of various layers, their interconnections etc.) and θ to denote the parameters (synaptic weights) of the deep network. L denotes the loss function (for example, cross-entropy loss in case of classification).CC BY · PubMed Central
Figure 5 Some implications of using domain-knowledge for commonly-used deep network architectures. Although attention-mechanism has also been used recently in many deep network architectures, we mention it only for RNNs and transformers as it is more prominently being used for sequence learning.
Figure 5 Some implications of using domain-knowledge for commonly-used deep network architectures. Although attention-mechanism has also been used recently in many deep network architectures, we mention it only for RNNs and transformers as it is more prominently being used for sequence learning.CC BY · PubMed Central
Figure 6 Introducing background knowledge into deep network by transforming data. T is a transformation block that takes input data D, background knowledge (BK) and outputs transformed data D^(′) that is then used to construct a deep model using a learner L.
Figure 6 Introducing background knowledge into deep network by transforming data. T is a transformation block that takes input data D, background knowledge (BK) and outputs transformed data D^(′) that is then used to construct a deep model using a learner L.CC BY · PubMed Central
Figure 7 Introducing background knowledge into deep network by transforming the loss function L. T block takes an input loss L and outputs a new loss function L^(′) by transforming (augmenting or modifying) L based on background knowledge (BK). The learner L then constructs a deep model using the original data D and the new loss function L^(′).
Figure 7 Introducing background knowledge into deep network by transforming the loss function L. T block takes an input loss L and outputs a new loss function L^(′) by transforming (augmenting or modifying) L based on background knowledge (BK). The learner L then constructs a deep model using the original data D and the new loss function L^(′).CC BY · PubMed Central
Figure 8 Introducing background knowledge into deep network by transforming the model (structure and parameter). In (a), the transformation block T takes a input structure of a model π and outputs a transformed structure π^(′) based on background knowledge (BK). In (b), the transformation block T takes a set of parameters θ of a model and outputs a transformed set of parameters π^(′) based on background knowledge (BK).
Figure 8 Introducing background knowledge into deep network by transforming the model (structure and parameter). In (a), the transformation block T takes a input structure of a model π and outputs a transformed structure π^(′) based on background knowledge (BK). In (b), the transformation block T takes a set of parameters θ of a model and outputs a transformed set of parameters π^(′) based on background knowledge (BK).CC BY · PubMed Central

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Citations

Cites

Show all 112 references

Research works printed but not linked: 9

  1. Lipton, Z. C. The mythos of model interpretability. arXiv preprint arXiv:1606.03490 (2016). · arXiv
  2. Garcez, A. S. d., Broda, K. B. & Gabbay, D. M. Neural-symbolic learning systems: foundations and applications (Springer, 2012).
  3. Radford, A. et al. Language models are unsupervised multitask learners. OpenAI Blog. 1, 9 (2019).
  4. Ellis, K., Morales, L., Meyer, M. S., Solar-Lezama, A. & Tenenbaum, J. B. Dreamcoder: Bootstrapping domain-specific languages for neurally-guided bayesian program learning. In Proceedings of the 2nd Workshop on Neural Abstract Machines and Program Induction (2018).
  5. Hogan, A. et al. Knowledge graphs. arXiv preprint arXiv:2003.02320 (2020). · arXiv
  6. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT (1), 4171–4186 (2019). https://aclweb.org/anthology/papers/N/N19/N19-1423/.
  7. Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R. & Borgwardt, K. M. Graph kernels. J. Mach. Learn. Res. 11, 1201–1242 (2010).
  8. Sourek, G., Aschenbrenner, V., Zelezny, F., Schockaert, S. & Kuzelka, O. Lifted relational neural networks: Efficient learning of latent relational structures. J. Artif. Intell. Res. 62, 69–100 (2018). · DOI
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Metadata

Authors
Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A.
Affiliations
Birla Institute of Technology and Science, Pilani - Goa Campus (IN)
Corresponding author
Dash, Tirtharaj
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2022-01-20
Volume
12
Issue
1
Pages
1040-1040
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-021-04590-0
PMC
PMC8776800
OpenAlex
W4205511145
Semantic Scholar
8f00fca03482af9c7aaf3c5ff60f8a0f69178935
Cited by
196
References
112
Citations by year
2026: 30 · 2025: 52 · 2024: 52 · 2023: 49 · 2022: 11
FWCI
13.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • University of New South Wales
  • Birla Institute of Technology and Science, Pilani
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
Keywords
domain knowledge, deep neural network, loss functions, network architecture, input encoding, human-machine collaboration

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