The Auto-Diagnosis of Granulation of Information Retrieval on the Web
Abstract
:1. Introduction
- Determining the auto-diagnosis of the concept ‘I’ for the auto-diagnosis agent,
- Whether the frequency of auto-diagnosis cycles (frequency of updating the auto-diagnosis history) should be consistent with the frequency of 40 Hz of the thalamus–cortex cycles,
- Whether the frequency of synchronization of auto-diagnosis cycles with the stability of auto-diagnosis history should correspond to brain waves: alpha (8–12 Hz), beta (above 12 Hz), theta (4–8 Hz), delta (0.5–4 Hz).
2. Conceiving of Assertions on the Web
- rdfs: Resource—class containing all resources,
- rdfs: Class—class containing all classes and their instances,
- rdf: Property—class containing all properties,
- and other rdfs: Literal, rdfs: Datatype, rdf: langString, rdf: HTML and rdf: XMLLiteral.
- Positive coupling is satisfied if the consequent is satisfied.
- Negative coupling is satisfied if it is excluded that the antecedent is satisfied, when the consequent is satisfied.
- Positive feedback is satisfied if the consequent is satisfied.
- Negative feedback is satisfied if it is excluded that the antecedent is satisfied, when the consequent is satisfied.
3. Compatibility of the Ontology Expressions with the Thesaurus Expressions
3.1. Algorithm for the Compatibility of Ontology Expressions with Thesaurus Expressions
Algorithm 1 |
3.2. Deduction
3.3. Analysis
3.4. Reduction
3.5. Synthesis
4. Assertion Perception Systems in de Morgan Algebras
4.1. Triangular Norms of Deduction
4.1.1. Deduction Norms
- boundary conditions
- monotonicity
- commutativity
- associativity
- there exists .
- boundary conditions
- monotonicity
- commutativity
- associativity
- there is
4.1.2. Analysis Norms
4.1.3. Reduction Norms
4.1.4. Synthesis Norms
4.1.5. De Morgan Algebra
- the algebra of deduction:
- the algebra of analysis:
- the algebra of reduction:
- the algebra of synthesis:
4.2. Perception Systems
- Let , then ,
- Let , then ,
- Let , then ,
- Let , then ,
- Let , then .
5. Conclusions
Funding
Conflicts of Interest
References
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Bryniarska, A. The Auto-Diagnosis of Granulation of Information Retrieval on the Web. Algorithms 2020, 13, 264. https://doi.org/10.3390/a13100264
Bryniarska A. The Auto-Diagnosis of Granulation of Information Retrieval on the Web. Algorithms. 2020; 13(10):264. https://doi.org/10.3390/a13100264
Chicago/Turabian StyleBryniarska, Anna. 2020. "The Auto-Diagnosis of Granulation of Information Retrieval on the Web" Algorithms 13, no. 10: 264. https://doi.org/10.3390/a13100264
APA StyleBryniarska, A. (2020). The Auto-Diagnosis of Granulation of Information Retrieval on the Web. Algorithms, 13(10), 264. https://doi.org/10.3390/a13100264