Imprecise Ontology Merging Framework Design

2011 ◽  
Vol 135-136 ◽  
pp. 578-584
Author(s):  
Guan Yu Li ◽  
Yan Zhao ◽  
Hai Yan Li
Keyword(s):  
Semantic Web ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Rough Set Theory ◽  
Distributed Environment ◽  
Ontology Merging ◽  
Web Environment ◽  
Framework Design ◽  
Efficient Measure

Precision is selected unwillingly by human being when dealing with imprecise objects because of the limitation of human cognitive ability, which deviates from the substance of the processed object when it gets the feasible way of solution. Nowadays, in terms of the research in the Ontology and the Semantic Web, the time for the transformation from the “precise phase” to the “imprecise phase” is ripe. The interoperability among ontologies is seriously blocked by the heterogeneity of ontologies constructed under distributed environment. In this case, Ontology merging in the same domain is the most effective method to solve ontology heterogeneity. Firstly, the improved fuzziness and the R-improved roughness are respectively defined and verified as the more efficient measure way for the fuzziness and roughness. Secondly, a composite appraisal method of fuzzy-rough relevancy in combination of the fuzzy set theory and the rough set theory is proposed, which can serve as the basis of the inquiry and reasoning of the imprecise ontology, the transformation reference of the fuzzy roughness set or the rough fuzziness set. Lastly, by employing semantic bridge generator and conflict processor, a novel multiple-mapping-based imprecise ontology merging framework is proposed. The example verification reveals that both the imprecise ontology merging efficiency can be improved and the merging source imprecise ontologies into object imprecise ontology can be done automatically under the semantic web environment.

scholarly journals Topology in the Alternative Set Theory and Rough Sets via Fuzzy Type Theory

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 432 ◽  
Author(s):  
Vilém Novák
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Rough Sets ◽  
Type Theory ◽  
Rough Set Theory ◽  
Basic Concepts ◽  
Alternative Set

In this paper, we will visit Rough Set Theory and the Alternative Set Theory (AST) and elaborate a few selected concepts of them using the means of higher-order fuzzy logic (this is usually called Fuzzy Type Theory). We will show that the basic notions of rough set theory have already been included in AST. Using fuzzy type theory, we generalize basic concepts of rough set theory and the topological concepts of AST to become the concepts of the fuzzy set theory. We will give mostly syntactic proofs of the main properties and relations among all the considered concepts, thus showing that they are universally valid.

scholarly journals A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

2021 ◽  
Vol 182 (2) ◽  
pp. 111-179
Author(s):  
Zaineb Chelly Dagdia ◽  
Christine Zarges
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Search Space ◽  
Locality Sensitive Hashing ◽  
Data Dependency ◽  
Distributed Environment ◽  
The Universe

In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data feature selection. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH-dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH-dRST is scalable when dealing with large amounts of data. We also demonstrate that LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost.

Application of Uncertainty Models in Bioinformatics

2016 ◽  
pp. 169-182 ◽  
Author(s):  
B.K. Tripathy ◽  
R.K. Mohanty ◽  
Sooraj T.R.
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Rough Set Theory ◽  
Life Sciences ◽  
Uncertain Information ◽  
Main Emphasis ◽  
Vague Data ◽  
Uncertainty Models ◽  
Theoretical Results

This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter is to present the general ideas for the time line of different uncertainty models to handle uncertain information and their applications in the various fields of biology. There are many mathematical models to handle vague data and uncertain information such as theory of probability, fuzzy set theory, rough set theory, soft set theory. Literatures from the life sciences and bioinformatics have been reviewed and provided the different experimental & theoretical results to understand the applications of uncertain models in the field of bioinformatics.

On Connections of Soft Set Theory with Existing Mathematics of Uncertainties: A Short Discussion for Non-Mathematicians with Respect to Soft Set Theory

2018 ◽  
Vol 14 (01) ◽  
pp. 1-9 ◽  
Author(s):  
Santanu Acharjee
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Rough Set Theory ◽  
Mathematical Structure ◽  
Soft Set ◽  
Hybrid Structures ◽  
Hesitant Fuzzy Set ◽  
Short Discussion

This paper focuses on two very important questions: “what is the future of a hybrid mathematical structure of soft set in science and social science?” and “why should we take care to use hybrid structures of soft set?”. At present, these are the most fundamental questions; which encircle a few prominent areas of mathematics of uncertainties viz. fuzzy set theory, rough set theory, vague set theory, hesitant fuzzy set theory, IVFS theory, IT2FS theory, etc. In this paper, we review connections of soft set theory and hybrid structures in a non-technical manner; so that it may be helpful for a non-mathematician to think carefully to apply hybrid structures in his research areas. Moreover, we must express that we do not have any intention to nullify contributions of fuzzy set theory or rough set theory, etc. to mankind; but our main intention is to show that we must be careful to develop any new hybrid structure with soft set. Here, we have a short discussion on needs of artificial psychology and artificial philosophy to enrich artificial intelligence.

Application of Uncertainty Models in Bioinformatics

Biotechnology ◽  
2019 ◽  
pp. 141-155
Author(s):  
B.K. Tripathy ◽  
R.K. Mohanty ◽  
Sooraj T. R.
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Rough Set Theory ◽  
Life Sciences ◽  
Uncertain Information ◽  
Main Emphasis ◽  
Vague Data ◽  
Uncertainty Models ◽  
Theoretical Results

This chapter provides the information related to the researches enhanced using uncertainty models in life sciences and biomedical Informatics. The main emphasis of this chapter is to present the general ideas for the time line of different uncertainty models to handle uncertain information and their applications in the various fields of biology. There are many mathematical models to handle vague data and uncertain information such as theory of probability, fuzzy set theory, rough set theory, soft set theory. Literatures from the life sciences and bioinformatics have been reviewed and provided the different experimental & theoretical results to understand the applications of uncertain models in the field of bioinformatics.

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