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Figure 1 The structure of the OWL2 (see online version for colours) 2.1 Obesity and cancer risk

Figure 1 The structure of the OWL2 (see online version for colours) 2.1 Obesity and cancer risk

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Source: Obesity and Cancer Risk — National Cancer Institute (2015) Table 1 The Guidelines of BMI
Source: Obesity and Cancer Risk — National Cancer Institute (2015) Table 1 The Guidelines of BMI
Note: *Relative risk estimates are summarised from the literature cited in the main text. Source: Calle and Kaaks (2004) Table 2 Obesity-related cancer
Note: *Relative risk estimates are summarised from the literature cited in the main text. Source: Calle and Kaaks (2004) Table 2 Obesity-related cancer
Note: *This tumour marker test is not applied in Egypt. ‘able 3 The cancer types and their related tumour markers
Note: *This tumour marker test is not applied in Egypt. ‘able 3 The cancer types and their related tumour markers
Source: OncoLink — The Web’s First Cancer Resource (2015) rable 4 Tumour markers reference ranges
Source: OncoLink — The Web’s First Cancer Resource (2015) rable 4 Tumour markers reference ranges
Table 5 The diabetes reference ranges
Table 5 The diabetes reference ranges
Figure 2 The proposed framework for building the fuzzy ontology of ORC domain (see online version for colours) We constructed our work in three main phases. First, we built the typical ontology by using Protégé 4.3 to allow reasoning using standard ontology reasoners, validation, and evaluation. The output of this phase is a validated ontology for obesity-related cancer with no fuzzy. Second, we represented the fuzzy data types and overlapping concepts by using OWL2 and fuzzy annotation properties through FuzzyOWL2 plug-in (Bobillo and Straccia, 2011). Finally, we reasoned with/ queried the constructed fuzzy ontology using fuzzyDL.
Figure 2 The proposed framework for building the fuzzy ontology of ORC domain (see online version for colours) We constructed our work in three main phases. First, we built the typical ontology by using Protégé 4.3 to allow reasoning using standard ontology reasoners, validation, and evaluation. The output of this phase is a validated ontology for obesity-related cancer with no fuzzy. Second, we represented the fuzzy data types and overlapping concepts by using OWL2 and fuzzy annotation properties through FuzzyOWL2 plug-in (Bobillo and Straccia, 2011). Finally, we reasoned with/ queried the constructed fuzzy ontology using fuzzyDL.
Source: Elhefny et al. (2014) Figure 3 ORC ontology building methodology (see online version for colours)
Source: Elhefny et al. (2014) Figure 3 ORC ontology building methodology (see online version for colours)
‘DO’ stated that the ovarian cancer has synonyms such as malignant ovarian tumour, neoplasm of ovary (disorder), ovarian neoplasm, ovary cancer, a tumour of the ovary, ... etc., To reduce the time and effort, we considered the term ‘ovarian cancer’ (that is a subclass of Female reproductive _organ_cancer) as a member (individual) of its superclass cancer, without taking into account the very detailed synonyms and sub-items, as the experts recommended, so we also did for some other diseases.
‘DO’ stated that the ovarian cancer has synonyms such as malignant ovarian tumour, neoplasm of ovary (disorder), ovarian neoplasm, ovary cancer, a tumour of the ovary, ... etc., To reduce the time and effort, we considered the term ‘ovarian cancer’ (that is a subclass of Female reproductive _organ_cancer) as a member (individual) of its superclass cancer, without taking into account the very detailed synonyms and sub-items, as the experts recommended, so we also did for some other diseases.
Notes: “Domain is a built-in property that links a property to a class description. >The range is a built-in property that links a property to either a class description or a data range. able 7 The object properties of the obesity-related cancers ontology
Notes: “Domain is a built-in property that links a property to a class description. >The range is a built-in property that links a property to either a class description or a data range. able 7 The object properties of the obesity-related cancers ontology
Figure 6 The ORC full ontology classes (see online version for colours)
Figure 6 The ORC full ontology classes (see online version for colours)
igure 7 Diagnosis class and its members represented in ORC ontology (see online version for colours)
igure 7 Diagnosis class and its members represented in ORC ontology (see online version for colours)
re8 Selection of classes, data and object properties in the ontology (partial) (see online version for colours) 4.1.4 Ontology validation and metrics
re8 Selection of classes, data and object properties in the ontology (partial) (see online version for colours) 4.1.4 Ontology validation and metrics
Figure 10 The typical ontology metrics (Elhefny et al., 2014) vs. our modified ORC ontology metrics in Protégé (continued) (see online version for colours)
Figure 10 The typical ontology metrics (Elhefny et al., 2014) vs. our modified ORC ontology metrics in Protégé (continued) (see online version for colours)
Figure 12 The fuzzy sets for underweight, normal, and overweight BMI (see online versio! for colours) Figure 13 The underweight fuzzy data type representation (as an example) (see online version for colours)
Figure 12 The fuzzy sets for underweight, normal, and overweight BMI (see online versio! for colours) Figure 13 The underweight fuzzy data type representation (as an example) (see online version for colours)
Related topics:
Knowledge Representation and ReasoningFuzzy Knowledge representation and reasoningDisease OntologyFuzzy Ontology
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