Shades of Knowledge-Infused Learning for Enhancing Deep Learning

Sheth, Amit P.; Gaur, Manas; Kursuncu, Ugur; Wickramarachchi, Ruwan

doi:10.1109/mic.2019.2960071

Cited by 45 publications

(26 citation statements)

References 12 publications

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“…Machine learning has been more and more widely used in image recognition 1 , speech recognition 2 , natural language processing 3 and other fields 4 – 6 . Machine learning can be divided into 7 , unsupervised learning 8 , semi-supervised learning 9 , and enhanced learning 10 . Supervised learning means a learning method that assigns labels or labels to training data.…”

Section: Introductionmentioning

confidence: 99%

Hybrid neural network based on novel audio feature for vehicle type identification

Chen

Zhang

2021

Sci Rep

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Due to the audio information of different types of vehicle models are distinct, the vehicle information can be identified by the audio signal of vehicle accurately. In real life, in order to determine the type of vehicle, we do not need to obtain the visual information of vehicles and just need to obtain the audio information. In this paper, we extract and stitching different features from different aspects: Mel frequency cepstrum coefficients in perceptual characteristics, pitch class profile in psychoacoustic characteristics and short-term energy in acoustic characteristics. In addition, we improve the neural networks classifier by fusing the LSTM unit into the convolutional neural networks. At last, we put the novel feature to the hybrid neural networks to recognize different vehicles. The results suggest the novel feature we proposed in this paper can increase the recognition rate by 7%; destroying the training data randomly by superimposing different kinds of noise can improve the anti-noise ability in our identification system; and LSTM has great advantages in modeling time series, adding LSTM to the networks can improve the recognition rate of 3.39%.

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Section: Introductionmentioning

confidence: 99%

Hybrid neural network based on novel audio feature for vehicle type identification

Chen

Zhang

2021

Sci Rep

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“…The result of this study is to develop an expert-in-the-loop technology for web-based intervention for a terminal mental health condition, suicide, and perform technology evaluation from the perspective of explainability [ 32 , 92 ]. Through concrete outcomes defining the capability of TinvM and TvarM, it is evident a hybrid model is desired to estimate the likelihood of an individual to exhibit suicide-related ideations, suicide-related behaviors, or suicide attempts for precise intervention.…”

Section: Discussionmentioning

confidence: 99%

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

et al. 2021

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Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential—most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.

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“…However, R-GCNs are restricted to KGs to deal only with binary relations, albeit they could be extended to go beyond binary relations using hypergraph neural networks (Feng et al, 2019;Bai et al, 2021). A method proposed for incorporating knowledge-graphs into deep networks is termed as "knowledge-infused learning" (Sheth et al, 2019;Kursuncu et al, 2020). The work examines techniques for incorporating relations at various layers of deep networks (the authors categorise these as: shallow, semi-deep and deep infusion).…”

Section: Related Workmentioning

confidence: 99%

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

2021

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We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on mode-directed inverse entailment (MDIE) developed in Inductive Logic Programming (ILP). Given a data instance e and background knowledge B, MDIE identifies a most-specific logical formula ⊥ B (e) that contains all the relational information in B that is related to e. We represent ⊥ B (e) by a "bottom-graph" that can be converted into a form suitable for GNN implementations. This transformation allows a principled way of incorporating generic background knowledge into GNNs: we use the term 'BotGNN' for this form of graph neural networks. For several GNN variants, using real-world datasets with substantial background knowledge, we show that BotGNNs perform significantly better than both GNNs without background knowledge and a recently proposed simplified technique for including domain knowledge into GNNs. We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons that use features representing a "propositionalised" form of the background knowledge; and BotGNNs to a standard ILP based on the use of most-specific clauses. Taken together, these results point to BotGNNs as capable of combining the computational efficacy of GNNs with the representational versatility of ILP.

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Shades of Knowledge-Infused Learning for Enhancing Deep Learning

Cited by 45 publications

References 12 publications

Hybrid neural network based on novel audio feature for vehicle type identification

Hybrid neural network based on novel audio feature for vehicle type identification

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

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