94,370 research outputs found
Switcher-random-walks: a cognitive-inspired mechanism for network exploration
Semantic memory is the subsystem of human memory that stores knowledge of
concepts or meanings, as opposed to life specific experiences. The organization
of concepts within semantic memory can be understood as a semantic network,
where the concepts (nodes) are associated (linked) to others depending on
perceptions, similarities, etc. Lexical access is the complementary part of
this system and allows the retrieval of such organized knowledge. While
conceptual information is stored under certain underlying organization (and
thus gives rise to a specific topology), it is crucial to have an accurate
access to any of the information units, e.g. the concepts, for efficiently
retrieving semantic information for real-time needings. An example of an
information retrieval process occurs in verbal fluency tasks, and it is known
to involve two different mechanisms: -clustering-, or generating words within a
subcategory, and, when a subcategory is exhausted, -switching- to a new
subcategory. We extended this approach to random-walking on a network
(clustering) in combination to jumping (switching) to any node with certain
probability and derived its analytical expression based on Markov chains.
Results show that this dual mechanism contributes to optimize the exploration
of different network models in terms of the mean first passage time.
Additionally, this cognitive inspired dual mechanism opens a new framework to
better understand and evaluate exploration, propagation and transport phenomena
in other complex systems where switching-like phenomena are feasible.Comment: 9 pages, 3 figures. Accepted in "International Journal of
Bifurcations and Chaos": Special issue on "Modelling and Computation on
Complex Networks
Knowledge Organization Research in the last two decades: 1988-2008
We apply an automatic topic mapping system to records of publications in
knowledge organization published between 1988-2008. The data was collected from
journals publishing articles in the KO field from Web of Science database
(WoS). The results showed that while topics in the first decade (1988-1997)
were more traditional, the second decade (1998-2008) was marked by a more
technological orientation and by the appearance of more specialized topics
driven by the pervasiveness of the Web environment
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Neural simulation of a system that learns representations of sensory experience
The pyriform cortex forms stable representations of smells to allow their subsequent recognition. Clustering systems are shown to perform a similar function, so they provide a guide to understanding the operation of the pyriform. A neural model of a sample of pyriform cortex was built that adheres to most known biological constraints, including learning by long-term potentiation. Results of early simulations suggest some interesting properties. The effort has implications for the knowledge representations used in artificial intelligence work
Applying clustering based on rules on WHO-DAS II for knowledge discovery on functional disabilities
The senior citizens represent a fast growing proportion of the population in Europe and other developed areas. This increases the proportion of persons with disability and reducing quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed a clinical test WHO Disability Assessment Schedule, (WHO-DASII) that is understood to include physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge on the performance of the test WHO-DASII on the basis of a sample of neurological patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, a technique stablished on 1994 by Gibert which combines some Inductive Learning (from AI) methods with Statistics to extract knowledge on ill-structured domains (that is complex domains where consensus is not achieved, like is the case). So, in this paper, the results of applying this technique to the WHO-DASII results is presented.Postprint (published version
Clustering tales from the Greek construction sector: lessons from experience
The idea of increasing regional and national economic competitiveness through the implementation of cluster strategies is not something new. In each business sector, in each country, the creation of clusters has been used to capitalise on sector characteristics and address country specific productivity needs. While clusters have met with significant success in many context, the Greek context and in particularly the Greek Construction sector has not been so fruitful. This paper, through the development of a conceptual framework, questionnaires with 92 firms and interviews with 10 key firms, sought to investigate the critical success factors for the creation of a cluster within the challenging context of the Greek construction sector. Using evidence of good practicefrom other European countries facing similar challenges and the empirical data, the findings indicated a series of factors which firms could adopt, mitigate against or manage to help improve the potential success of the cluster. The findingstherefore have important implications for interventions not only by the state and local authorities that will encourage construction firms to participate in a cluster, but also by the managers/owners/practitioners for the creation of the required foundations for their participation in an environment where competitors cooperate
Models of incremental concept formation
Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions
Identifying Metaphor Hierarchies in a Corpus Analysis of Finance Articles
Using a corpus of over 17,000 financial news reports (involving over 10M
words), we perform an analysis of the argument-distributions of the UP- and
DOWN-verbs used to describe movements of indices, stocks, and shares. Using
measures of the overlap in the argument distributions of these verbs and
k-means clustering of their distributions, we advance evidence for the proposal
that the metaphors referred to by these verbs are organised into hierarchical
structures of superordinate and subordinate groups
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Recognition by directed attention to recursively partitioned images
A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth
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