**4.2 Prototype-based ontology**

A prototype-based ontology is described as a terminological ontology whose categories are distinguished by typical instances or *prototypes* rather than by axioms and definitions in logic [16].

For every category *c* in a prototype-based ontology, there must be a prototype *p* and a measure of *semantic distance* d(*x, y, c*), which computes the dissimilarity between two entities *x* and *y* when they are considered instances of *c*. Then an entity *x* can be classified by the following recursive procedure:


Running: A Mixed Language Software

Fig. 2. Input ontology

Economic nature

Project l Project 1

Fig. 3. Output ontology

Program

Investment budjet

> External resources

Donation Loans

article

Objective

Objective 1

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State budjet

Functioning budjet

Internal resources

> Non specialized internal resources

Principal function i

State budjet

Sector n Sector 1

Specialized internal resources

Sector Ministerial Budjetary chapter

Secondary function

function 1 Secondary

function j

Secondary

Objective k

Principal function 1

Principal function

*Since a prototype-based ontology depends on examples, it is often convenient to derive the semantic distance measure by a method that learns from examples, such as statistics, cluster analysis, or neural networks.*

We may allow an individual in our ontology to have multiple prototypes. Therefore we represent this relationship between Individuals with some hasPrototype property. So we need a way to define multiple inheritance mechanism on instances (and that is exactly what the hasPrototype property would do).

For the translation to be performed, some text analysis is required. Keyword counts are a simple analytical technique, but they ignore sentence structure. This is why a prototypebased ontology can be necessary in order to give a normal sense to sentences. While considering a sentence which is a text as an object, we can define prototypes on it. Moreover a sentence drains an idea and can be limited to a single term.

#### **Example 1**

There may be an object Regional\_HospitalA in region A. Then, we proceed to create object Regional\_HospitalB by specifying Regional\_ HospitalA as its prototype, as well as specifying that Regional\_HospitalB is situated in region B.

We precise that Regional\_HospitalB has the same properties as Regional\_HospitalA, except that its region is B.

We could choose to describe it like this:

```
 Individuals = {Regional_HospitalA, Regional_HospitalB, A, B} 

Regional_HospitalA hasRegion A 
Regional_HospitalB hasRegion B 

Regional_HospitalB hasPrototype Regional_HospitalA
```
While reasoning, we ignore the inherited property value if the object redefines it itself. In this case, the information that Regional\_HospitalB is in region B should have priority over the information that Regional\_HospitalB may be situated in region A because it is like Regional\_HospitalA.

If there is no translated information on region B to be recorded by the user during its work, search through input ontology (investments list in the investment budget) and output ontology (projects list in the appropriate program) helps to solve the problem.

#### **Input ontology**

We consider the concept All State revenue. It can represent the foundation of the input ontology illustrated in Figure2.

#### **Output ontology**

We consider the concept « All State expenditure ». It can constitute the basis of the output ontology presented in Figure3.

*Since a prototype-based ontology depends on examples, it is often convenient to derive the semantic distance measure by a method that learns from examples, such as statistics, cluster analysis, or neural* 

We may allow an individual in our ontology to have multiple prototypes. Therefore we represent this relationship between Individuals with some hasPrototype property. So we need a way to define multiple inheritance mechanism on instances (and that is exactly what

For the translation to be performed, some text analysis is required. Keyword counts are a simple analytical technique, but they ignore sentence structure. This is why a prototypebased ontology can be necessary in order to give a normal sense to sentences. While considering a sentence which is a text as an object, we can define prototypes on it. Moreover

There may be an object Regional\_HospitalA in region A. Then, we proceed to create object Regional\_HospitalB by specifying Regional\_ HospitalA as its prototype, as well as

We precise that Regional\_HospitalB has the same properties as Regional\_HospitalA, except

While reasoning, we ignore the inherited property value if the object redefines it itself. In this case, the information that Regional\_HospitalB is in region B should have priority over the information that Regional\_HospitalB may be situated in region A because it is like

If there is no translated information on region B to be recorded by the user during its work, search through input ontology (investments list in the investment budget) and output

We consider the concept All State revenue. It can represent the foundation of the input

We consider the concept « All State expenditure ». It can constitute the basis of the output

Individuals = {Regional\_HospitalA, Regional\_HospitalB, A, B}

Regional\_HospitalB hasPrototype Regional\_HospitalA

ontology (projects list in the appropriate program) helps to solve the problem.

*networks.*

**Example 1** 

that its region is B.

Regional\_HospitalA.

**Input ontology** 

**Output ontology** 

ontology illustrated in Figure2.

ontology presented in Figure3.

the hasPrototype property would do).

We could choose to describe it like this:

Regional\_HospitalA hasRegion A Regional\_HospitalB hasRegion B

a sentence drains an idea and can be limited to a single term.

specifying that Regional\_HospitalB is situated in region B.

Fig. 3. Output ontology

Running: A Mixed Language Software

**5.2 Learning in Case-Based Reasoning** 

order to avoid the same mistake in the future.

**Example 2** 

prototype.

except that its region is B.

as an e-Learning Solution for the State Budget Management 267

Some systems retrieve cases based largely on superficial syntactic similarities among

Reusing the retrieved case solution in the context of the new case focuses on: identifying the differences between the retrieved and the current case; and identifying the part of a retrieved case which can be transferred to the new case. Generally the solution of the

Revising the case solution generated by the reuse process is necessary when the solution

Retaining the case is the process of incorporating whatever is useful from the new case into the case library. This involves deciding what information to retain and in what form to retain it; how to index the case for future retrieval; and integrating the new case into the case library.

A very important feature of case-based reasoning is its coupling to learning. CBR is an approach to incremental, sustained learning, since a new experience is retained each time a problem has been solved, making it immediately available for future problems. Learning in CBR occurs as a natural by-product of problem solving. When a problem is successfully solved, the experience is retained in order to solve similar problems in the future. When an attempt to solve a problem fails, the reason for the failure is identified and remembered in

Case-based reasoning allows learning from experience, since it is usually easier to learn by

The establishment of prototype-based ontology lays the foundation for case knowledge sharing. The tradeoff associates prototype-based ontology and case based reasoning technology in State budget management. The system structure of translation process is designed on the basis of prototype-based ontology and case-based reasoning theory and its application. The system not only considers the full use of bilingual domain experts' experiences and knowledge, but also can supports sharing and reuse of case knowledge in budgetary case bases. So, it solves the problem of knowledge reuse generated for those learning one or the other language with focus on budgetary domain. Also a simple semantic similarity algorithm can be admitted and used to compute similarity between new case and a case from case bases. So the real meaning of each term or sentence in different case expression is discovered and they are recorded in case bases with their mapping relation.

We suppose that there exists an object Regional\_RoadA1A2 in region A. Then, we need to create object Regional\_RoadB1B2 in region B by specifying Regional\_ RoadA1A2 as its

We indicate that Regional\_RoadB1B2 has the same properties as Regional\_RoadA1A2,

retaining a concrete problem solving experience than to generalize from it.

**5.3 Tradeoff between prototype-based ontology and case-based reasoning** 

problem descriptors, while advanced systems use semantic similarities.

retrieved case is transferred to the new case directly as its solution case.

proves incorrect. This provides an opportunity to learn from failure.

#### **Budgetary ontology**

The finance law is a law of the land and provides for and authorizes all State revenue and expenditure for the upcoming fiscal year. It is founded on a budgetary ontology presented in figure….

Fig. 4. Budgetary ontology
