2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC) 2017
DOI: 10.1109/icci-cc.2017.8109729
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Building semantic hierarchies of formal concepts by deep cognitive machine learning

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Cited by 6 publications
(5 citation statements)
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“…One of the key breakthroughs of κC is autonomous knowledge learning enabled by Level-5 cognitive intelligence according to Figure 5 (Wang, 2022a), (Valipour and Wang, 2017). According to HIM, the abilities for knowledge learning and reasoning are an indispensable foundation for intelligence generation by κC.…”
Section: Paradigms Of κC For Autonomous Knowledge Learning and Machin...mentioning
confidence: 99%
See 1 more Smart Citation
“…One of the key breakthroughs of κC is autonomous knowledge learning enabled by Level-5 cognitive intelligence according to Figure 5 (Wang, 2022a), (Valipour and Wang, 2017). According to HIM, the abilities for knowledge learning and reasoning are an indispensable foundation for intelligence generation by κC.…”
Section: Paradigms Of κC For Autonomous Knowledge Learning and Machin...mentioning
confidence: 99%
“…In autonomous knowledge learning, κC adopts a relational neural cluster structure for knowledge representation in CKB as illustrated in Figure 4. The set of knowledge acquired by κC for the first 600+ frequently used concepts in English is shown in Figure 11 (Wang and Valipour, 2016;Valipour and Wang, 2017;. It only takes the κC less than a minute to quantitatively acquire such scope of knowledge represented by a network of quantitative semantic relations, which would normally require several months by traditional qualitative ontology-extraction in forms of non-quantitative or subjective knowledge graphs without adopting rigorous IM underpinned by concept algebra (Wang, 2010) and semantic algebra (Wang, 2013).…”
Section: Figurementioning
confidence: 99%
“…Because ANNs can iteratively learn their own features, use large amounts of data, and are less constrained by assumptions about that data, they are extremely flexible and can handle many kinds of tasks [e.g., [41][42][43][44][45][46][47]. These methods have been used on a variety of topics including image processing, video segmentation, and speech recognition [48][49][50].…”
Section: Machine Learningmentioning
confidence: 99%
“…1996, Sprengel et al, 2016Zell, 1994;O'Mahony et al, 2020;LeCun et al, 2015;Alom et al, 2018). These methods have been used on a variety of topics including image processing, video segmentation, and speech recognition (Hemanth & Estrela, 2017;Valipour & Wang, 2017;Kamath et al, 2019). Although challenges remain with respect to scalability, computational efficiency, and how to handle depauperate data (Alom et al, 2018), deep learning is one of the most powerful analytical tools in the modern researcher's toolbox, particularly when human knowledge is lacking, or datasets are too large to be workable by traditional means.…”
Section: Machine Learningmentioning
confidence: 99%
“…Because ANNs can iteratively learn their own features, use large amounts of data, and are less constrained by assumptions about that data, they are extremely flexible and can handle many kinds of tasks [e.g., 36, 37, 38, 39, 40, 41, 42]. These methods have been used on a variety of topics including image processing, video segmentation, and speech recognition [43, 44, 45]. Although challenges remain with respect to scalability, computational efficiency, and how to handle depauperate data [42], deep learning is one of the most powerful analytical tools in the modern researcher’s toolbox, particularly when human knowledge is lacking, or datasets are too large to be workable by traditional means.…”
Section: Introductionmentioning
confidence: 99%