Abstract: In a highly competitive environment, it becomes more
important to shorten the whole business process while delivering or
even enhancing the business value to the customers and suppliers.
Although the workflow management systems receive much attention
for its capacity to practically support the business process enactment,
the effective workflow modeling method remain still challenging and
the high degree of process complexity makes it more difficult to gain
the short lead time. This paper presents a workflow structuring method
in a holistic way that can reduce the process complexity using
activity-needs and formal concept analysis, which eventually enhances
the key performance such as quality, delivery, and cost in business
process.
Abstract: In order to integrate knowledge in heterogeneous
case-based reasoning (CBR) systems, ontology-based CBR system
has become a hot topic. To solve the facing problems of
ontology-based CBR system, for example, its architecture is
nonstandard, reusing knowledge in legacy CBR is deficient, ontology
construction is difficult, etc, we propose a novel approach for
semi-automatically construct ontology-based CBR system whose
architecture is based on two-layer ontology. Domain knowledge
implied in legacy case bases can be mapped from relational database
schema and knowledge items to relevant OWL local ontology
automatically by a mapping algorithm with low time-complexity. By
concept clustering based on formal concept analysis, computing
concept equation measure and concept inclusion measure, some
suggestions about enriching or amending concept hierarchy of OWL
local ontologies are made automatically that can aid designers to
achieve semi-automatic construction of OWL domain ontology.
Validation of the approach is done by an application example.
Abstract: This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from positive and negative association rules using the Concept lattice structure of the Formal Concept Analysis. Context reduction technique given in Formal Concept Analysis along with classification rules has been used to find redundancies among the various medical examination tests. Also it finds out whether expensive medical tests can be replaced by some cheaper tests.
Abstract: Medical Decision Support Systems (MDSSs) are sophisticated, intelligent systems that can provide inference due to lack of information and uncertainty. In such systems, to model the uncertainty various soft computing methods such as Bayesian networks, rough sets, artificial neural networks, fuzzy logic, inductive logic programming and genetic algorithms and hybrid methods that formed from the combination of the few mentioned methods are used. In this study, symptom-disease relationships are presented by a framework which is modeled with a formal concept analysis and theory, as diseases, objects and attributes of symptoms. After a concept lattice is formed, Bayes theorem can be used to determine the relationships between attributes and objects. A discernibility relation that forms the base of the rough sets can be applied to attribute data sets in order to reduce attributes and decrease the complexity of computation.
Abstract: The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.
Abstract: One of the most ancient humankind concerns is knowledge formalization i.e. what a concept is. Concept Analysis, a branch of analytical philosophy, relies on the purpose of decompose the elements, relations and meanings of a concept. This paper aims at presenting a method to make a concept analysis obtaining a knowledge representation suitable to be processed by a computer system using either object-oriented or ontology technologies. Security notion is, usually, known as a set of different concepts related to “some kind of protection". Our method concludes that a more general framework for the concept, despite it is dynamic, is possible and any particular definition (instantiation) depends on the elements used by its construction instead of the concept itself.