Perspectives on eBrain and Cognitive Computing

Wang, Yingxu; Anderson, James A.; Baciu, George; Budin, Gerhard; Hsu, David; Ishizuka, Mitsuru; Kinsner, W.; Mizoguchi, Fumio; Nishida, Toyoaki; Sugawara, Kenji; Tsumoto, Shusaku; Zhang, Du

doi:10.4018/jcini.2012100101

Cited by 20 publications

(4 citation statements)

References 77 publications

(28 reference statements)

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“…Zhang proposed a method to discover small-world in association link networks for web-based learning (Zhang, 2011). Wang proposed and developed Cognitive Computing Theory (Wang, 2011a;Wang, 2012a;Wang, 2012b) which provided the theory foundation for ontology and concept learning (Wang, 2011b). Wei used fuzzy methods to mine the online comments and assess the quality of hotel (Wei, 2015).…”

Section: Semantic Mining and Annotationmentioning

confidence: 99%

Building the Multidimensional Semantic Index of Webpages for Facet Extraction

Wei

Qin

2015

International Journal of Cognitive Informatics and Natural Intelligence

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Faceted search is an efficient search method to use the big data and one of its key issues is to extract facets from unstructured webpages automatically. It is still a problem to extract facets from massive unstructured webpages exactly and automatically. To solve the problem, this paper first proposed a novel index structure of webpages, the Multidimensional Semantic Index (MDSI), which holds rich semantics and are helpful to extract facets. In MDSI, the differently dimensional semantic indexes are bridged by mining the semantic mapping between them. Then, an automatic facet extraction method is proposed by analysing semantic mapping relations in MDSI. At last, to validate the effect of the proposed method, two datasets are constructed and the experimental results show that the proposed method is feasible and comparatively precise.

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Section: Semantic Mining and Annotationmentioning

confidence: 99%

Building the Multidimensional Semantic Index of Webpages for Facet Extraction

Wei

Qin

2015

International Journal of Cognitive Informatics and Natural Intelligence

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“…Cognitive informatics (Wang, 2002a(Wang, , 2003(Wang, , 2006(Wang, , 2007b(Wang, , 2007c(Wang, , 2007d(Wang, , 2009a(Wang, , 2009b(Wang, , 2012c(Wang, , 2012d(Wang, , 2012f, 2013aWang & Kinsner, 2006;Wang & Wang, 2006;Wang & Berwick, 2012b;Wang et al, 2009bWang et al, , 2009cWang et al, , 2011bWang et al, , 2011dWang et al, , 2012cWang et al, , 2012d is the third level of brain studies in the hierarchical framework of brain science, which is built on both neuroinformatics and brain informatics at the neurological and physiological levels towards abstract intelligence at the logical level.…”

Section: Cognitive Informaticsmentioning

confidence: 99%

Perspectives on Cognitive Computers and Knowledge Processors

Patel

Wang

Fariello

et al. 2013

International Journal of Cognitive Informatics and Natural Intelligence

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Cognitive Informatics (CI) is a contemporary multidisciplinary field spanning across computer science, information science, cognitive science, brain science, intelligence science, knowledge science, cognitive linguistics, and cognitive philosophy. CI aims to investigate the internal information processing mechanisms and processes of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing and computational intelligence. This paper reports a set of eleven position statements presented in the plenary panel of IEEE ICCI*CC’13 on Cognitive Computers and Knowledge Processors contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

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“…Cognitive informatics (Wang, 2002a(Wang, , 2003(Wang, , 2006(Wang, , 2007b(Wang, , 2007c(Wang, , 2007d(Wang, , 2009a(Wang, , 2009b(Wang, , 2012c(Wang, , 2012dWang & Kinsner, 2006;Wang & Wang, 2006;Wang & Berwick, 2012b;Wang et al, 2009bWang et al, , 2009cWang et al, , 2010Wang et al, , 2011bWang et al, , 2011dWang et al, , 2012cWang et al, , 2012d is the third level of brain studies in the hierarchical framework of brain science as illustrated in Figure 1, which is built on both neuroinformatics and brain informatics at the neurological and physiological levels towards αI at the logical level.…”

Section: Cognitive Informatics: the Functional Models Of The Brainmentioning

confidence: 99%

On Abstract Intelligence and Brain Informatics

Wang

2012

International Journal of Cognitive Informatics and Natural Intelligence

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A key notion in abstract intelligence and cognitive informatics is that the brain and natural intelligence may only be explained by a hierarchical and reductive theory that maps the brain through the embodied neurological, physiological, cognitive, and logical levels from bottom-up induction and top-down deduction. This paper presents an abstract intelligence framework for modeling the structures and functions of the brain across these four levels. A set of abstract intelligent model, cognitive functional model, and neurophysiological model of the brain is systematically developed. On the basis of the abstract intelligent models of the brain at different levels, the conventionally highly overlapped, redundant, and even contradicted empirical observations in brain studies and cognitive psychology may be rigorously clarified and neatly explained. The improved understanding about the brain has led to the development of a wide range of novel technologies and systems such as cognitive computers, cognitive robots, and other applied cognitive systems.

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Perspectives on eBrain and Cognitive Computing

Cited by 20 publications

References 77 publications

Building the Multidimensional Semantic Index of Webpages for Facet Extraction

Building the Multidimensional Semantic Index of Webpages for Facet Extraction

Perspectives on Cognitive Computers and Knowledge Processors

On Abstract Intelligence and Brain Informatics

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