CN113158654B - Domain model extraction method and device and readable storage medium - Google Patents

Abstract

The invention discloses a method, device and readable storage medium for extracting a domain model, wherein the method comprises: grammatically analyzing a requirement document to determine the dependency between word segments; determining the relationship between concepts according to the dependency between the word segments The semantic relationship between the concepts; the corresponding domain model is determined according to the semantic relationship between the concepts. In the present invention, the dependency relationship between the word segments in the requirement document is determined; the semantic relationship between concepts is determined according to the dependency relationship between the word segments; the corresponding domain model is determined according to the semantic relationship between the concepts, thereby improving the The extraction accuracy of the domain model.

Description

Domain model extraction method, device and readable storage medium

technical field

The present invention relates to the technical field of natural language recognition, and in particular, to a method, device and readable storage medium for extracting a domain model.

Background technique

A domain model is a visual representation of important concepts in the domain and their relationships, and is used in the analysis phase of software development to analyze how to meet the functional requirements of the system. Domain models can be represented using UML class diagrams, use case diagrams, ontologies, etc., as needed. The domain model mainly consists of concepts, attributes and relationships. Concepts represent entities or events in the real world. The attributes of concepts are logical data contained in entities represented by concepts. Various relationships between concepts represent semantic connections or interactive behaviors between entities represented by concepts. Common relationships include Association relationship, aggregation relationship, inheritance relationship, etc.

A domain model provides structured knowledge about the underlying terms that make up the domain. Also, the design of a system, especially in a model-based development environment, is often shaped around a domain model. Correctly identifying concepts and the relationship between concepts can help analyze the system architecture in the software development process, reduce development difficulty, reduce code redundancy, and help developers analyze the inconsistency and incompleteness of the requirements themselves. When developers build a domain model, they need to check the requirements document repeatedly to ensure that the established domain model is consistent with the requirements, and to ensure that all concepts and relationships related to the requirements are included in the domain model. For large applications, building a domain model manually is a daunting task.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method, an apparatus, and a readable storage medium for extracting a domain model, so as to improve the accuracy of extracting a domain model.

In a first aspect, an embodiment of the present invention provides a method for extracting a domain model, including:

Perform grammatical analysis on the requirements document to determine the dependencies between the word segmentations;

Determine the semantic relationship between concepts according to the dependency relationship between the word segmentations;

The corresponding domain model is determined according to the semantic relationship between the concepts.

Optionally, perform syntax analysis on the requirements document, including:

Decompose the requirement document to obtain the corresponding word segmentation;

Perform part-of-speech tagging based on the part-of-speech, and determine the corresponding part-of-speech type according to the part-of-speech tagging result;

Based on the word segmentation type, a dependency relationship between each word segmentation is determined.

Optionally, after determining the corresponding word segmentation type according to the part-of-speech tagging result, it also includes:

cleaning the participle;

Extract the word stems in the cleaning result;

A reduction process is performed on the participle stems.

Optionally, the semantic relationship between concepts is determined according to the dependency between the word segments, including:

Traverse the noun phrases in the participle, and determine the dependencies between phrases and words and between phrases;

Extract semantic relationships between concepts based on dependencies between phrases and words and between phrases.

Optionally, traverse the noun phrases in the word segmentation, and the dependencies between derived phrases and words and between phrases, including:

If the target node corresponding to the dependency relationship with the word in the current noun phrase as the source node falls into the current noun phrase, the current noun phrase is not derived;

If the target node corresponding to the dependency with the word in the current noun phrase as the source node falls outside the current noun phrase, the current noun phrase is derived.

Optionally, the current noun phrase is derived, including: if the derived word is a source node word in a noun phrase other than the current noun phrase, then the dependency relationship between the phrases is obtained by deriving, otherwise the phrase is obtained by deriving and word dependencies.

Optionally, extract semantic relationships between concepts based on dependencies between phrases and words and between phrases, including:

Extract the associations between concepts according to the source nodes corresponding to different grammatical structures according to the dependencies between phrases and words and between phrases; and,

Match the dependencies between phrases and words and between phrases according to the preset word structure, and identify the aggregation relationship, cardinality relationship and attribute relationship between concepts.

Optionally, the corresponding domain model is determined according to the association relationship between the concepts, including:

Traversing the boundary concepts in the association relationship between the concepts;

Modifying the association relationship of the boundary concepts that match the preset fields in the boundary concepts;

Wherein, the boundary concept is that there is only one other concept that has a semantic relationship with the boundary concept.

In a second aspect, an embodiment of the present invention provides an apparatus for extracting a domain model, including:

The analysis unit is used to syntactically analyze the requirement document and determine the dependency between the word segmentations;

a relationship determining unit, configured to determine the semantic relationship between concepts according to the dependency between the word segments;

A domain model determining unit, configured to determine a corresponding domain model according to the semantic relationship between the concepts.

In a third aspect, embodiments of the present invention provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, implements the steps of the foregoing method for extracting a domain model.

In the embodiment of the present invention, the dependency relationship between the word segments in the requirement document is determined; the semantic relationship between concepts is determined according to the dependency relationship between the word segments; the corresponding domain model is determined according to the semantic relationship between the concepts, thereby The extraction accuracy of the domain model is improved, and positive technical effects are achieved.

The above description is only an overview of the technical solutions of the present invention, in order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description, and in order to make the above and other objects, features and advantages of the present invention more obvious and easy to understand , the following specific embodiments of the present invention are given.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

Fig. 1 is a flow chart of the first embodiment of the present invention;

2 is a flow chart of syntax analysis according to the first embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a device according to a second embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art.

Example 1

The first embodiment of the present invention provides a method for extracting a domain model, as shown in FIG. 1 , including the following specific steps:

S101. Perform grammatical analysis on the requirement document to determine the dependency between the word segmentations;

S102, determining the semantic relationship between concepts according to the dependency between the word segments;

S103. Determine a corresponding domain model according to the semantic relationship between the concepts.

In the embodiment of the present invention, the dependency relationship between the word segments in the requirement document is determined; the semantic relationship between concepts is determined according to the dependency relationship between the word segments; the corresponding domain model is determined according to the semantic relationship between the concepts, thereby The extraction accuracy of the domain model is improved.

Optionally, perform syntax analysis on the requirements document, including:

Decompose the requirement document to obtain the corresponding word segmentation;

Perform part-of-speech tagging based on the part-of-speech, and determine the corresponding part-of-speech type according to the part-of-speech tagging result;

Based on the word segmentation type, a dependency relationship between each word segmentation is determined.

Specifically, in this embodiment, the syntactic analysis includes preprocessing of the statement of requirements, including word segmentation, sentence segmentation, part-of-speech tagging, phrase structure analysis, and dependency syntax analysis. In this embodiment, the input requirement document is grammatically analyzed, and the main process is shown in Figure 2, including the following content:

Clause: Divide the input text into individual sentences.

Word segmentation: Divide the input sentence into individual symbols. A symbol can be a word, a number, a punctuation or a space.

Part-of-speech tagging: tag the part-of-speech of the symbols obtained by the tokenizer, such as noun (NN), verb (VB), adjective (JJ), preposition (IN), article (DT), conjunction (CC), etc.

Phrase Structure Analysis: Infer the type of each structural unit in a sentence, such as Noun Phrase (NP), Verb Phrase (VP), Prepositional Phrase (PP), Verb (VB), Article (DT), Preposition (IN), etc. .

Dependency syntactic analysis: The grammatical relationship between independent words in a sentence is analyzed and represented by the dependency relationship. The input of dependency parsing is a sentence, and the output is a directed acyclic graph consisting of relation triples, for example represented as triples <word, dependency category, word>. According to the International Dependency Definition Framework Universal Dependencies, the dependency categories in this embodiment mainly include: noun-subject relationship (nsubj), passive noun-subject relationship (nsubjpass), direct object relationship (dobj), adjective modifier (amod), Nominal modifier (nmod), noun clause modifier (acl), relative clause modifier (acl:relcl), etc.

Among them, the nominal modifier (nmod) represents the prepositional phrase structure in the sentence. The noun clause modifier (acl) represents the complement structure of the infinitive or participle form of the verb; the relative clause modifier (acl:relcl) represents the clause modifier structure.

Optionally, after determining the corresponding word segmentation type according to the part-of-speech tagging result, it also includes:

cleaning the participle;

Extract the word stems in the cleaning result;

A reduction process is performed on the participle stems.

In this embodiment, after the word segmentation result is obtained, the grammatical analysis further includes removing stop words: wherein, stop words are words that frequently appear in the text and do not represent specific meanings, such as "a", "the", "any" Wait.

Stemming and lemmatization: Convert the plural forms of nouns, the participle forms of verbs, and the forms of adjectives to adverbs into the original forms of these words.

Extract atomic noun phrases and verbs in preparation for further extraction of concepts and relationships from the domain model.

Optionally, the semantic relationship between concepts is determined according to the dependency between the word segments, including:

Traverse the noun phrases in the participle, and determine the dependencies between phrases and words and between phrases;

Extract semantic relationships between concepts based on dependencies between phrases and words and between phrases.

Optionally, traverse the noun phrases in the word segmentation, and the dependencies between derived phrases and words and between phrases, including:

If the target node corresponding to the dependency relationship with the word in the current noun phrase as the source node falls into the current noun phrase, the current noun phrase is not derived;

If the target node corresponding to the dependency with the word in the current noun phrase as the source node falls outside the current noun phrase, the current noun phrase is derived.

Optionally, the current noun phrase is derived, including: if the derived word is a source node word in a noun phrase other than the current noun phrase, then the dependency relationship between the phrases is obtained by deriving, otherwise the phrase is obtained by deriving and word dependencies.

Specifically, in this embodiment, on the basis of the word segmentation obtained by the aforementioned grammatical analysis, the dependency relationship between the words obtained by the grammatical analysis is further derived to obtain the dependency relationship at the phrase level. Phrase-level dependencies can be represented as relation triples <phrase, dependency type, phrase> or <phrase, dependency type, word>.

The pseudo code of the dependency relationship derivation algorithm adopted in this embodiment is shown in Table 1.

Table 1 Dependency Derivation Algorithm

The input of the dependency relationship derivation algorithm in this embodiment is all words, noun phrases, and the dependencies between words obtained by grammatical analysis, and the output is the dependencies between phrases and between phrases and words. The specific process includes:

Check for all noun phrases NP in the requirements document:

For each word token ₁ in the noun phrase NP: if the target node of a dependency dep(token ₁ , token ₂ ) starting from this word as the source node still falls in this noun phrase, then this dependency is not performed. derived.

If the target node of the dependency falls outside the noun phrase, then the dependency dep is derived:

If token ₂ is part of another noun phrase NP ₂ , the derived dependency dep is dep(NP, NP ₂ ), otherwise the derived dependency dep is dep(NP, token ₂ ).

From this, dependencies between phrases and words and between phrases are determined.

Optionally, extract semantic relationships between concepts based on dependencies between phrases and words and between phrases, including:

Extract the associations between concepts according to the source nodes corresponding to different grammatical structures according to the dependencies between phrases and words and between phrases; and,

Match the dependencies between phrases and words and between phrases according to the preset word structure, and identify the aggregation relationship, cardinality relationship and attribute relationship between concepts.

Specifically, in this embodiment, the semantic relationship between concepts includes an association relationship, an aggregation relationship, a cardinality relationship, and an attribute relationship. The association relationship includes direct relationship and indirect relationship, wherein the direct relationship represents the relationship between concepts and concepts directly connected by a verb or verb phrase (including the participle form or infinitive form of the verb or verb phrase, etc.) or a preposition directly connected; indirect relationship If there is a direct relationship between concept A and concept B, and there is a direct relationship between concept B and concept C, then there is an indirect relationship between concept A and concept C.

Based on this, in this embodiment, the association relationship between concepts is extracted according to the source nodes corresponding to different grammatical structures according to the dependencies between phrases and words and between phrases, including: first identifying the direct relationship between concepts, and then according to the direct relationship. Indirect relationships between concepts are derived, resulting in all associations.

Specifically, according to the dependencies between phrases and words and between phrases, the associations between concepts are extracted according to source nodes corresponding to different grammatical structures, including:

For the direct relationship represented by the subject-verb-object structure, the subject is the source concept of the relationship, the object is the target concept of the relationship, and the predicate verb is the content of the relationship.

For the subject-predicate-object relationship in a relative clause, according to the acl:relcl dependency, find the noun phrase referred to by the subject that or which in the relative clause, as the source concept of the relationship, and the object and predicate in the clause as the target concept and content.

For the direct relationship represented by the prepositional phrase structure, the noun modifier (nmod) is used for extraction, and the pseudocode of the extraction algorithm is shown in Table 2.

Table 2 Pseudo code of preposition phrase extraction algorithm

Taking as input the set of all atomic noun phrases and verbs, check whether each noun phrase or verb is a source node of an nmod dependency.

If the source node of the nmod dependency is a noun phrase, the noun phrase of the source node of the nmod dependency is taken as the source concept of the relation, the noun phrase of the target node is taken as the target concept of the relation, and the preposition is taken as the content of the relation.

If the source node of the nmod dependency is a verb, the direct object of this verb is taken as the source concept of the relation, the noun phrase of the target node of the nmod dependency is taken as the target concept of the relation, and the preposition is taken as the content of the relation.

For the direct relationship represented by the verb complement structure, the clause modifier (acl) of the noun is used to extract, and the pseudo code of the extraction algorithm is shown in Table 3.

Table 3 Pseudo code of verb complement extraction algorithm

Take the set of all atomic noun phrases as input, and check whether each noun phrase is the source node of an acl dependency.

If the noun phrase is the source node of an acl dependency, and the target node of the acl dependency is a transitive verb or verb phrase, then the noun phrase of the source node of the acl dependency is used as the source concept of the relationship, and the dependency target node The object followed by the verb or verb phrase is the target concept of the relation, and the verb or verb phrase is the content of the relation.

According to the direct relationship extracted above, the indirect relationship between concepts is deduced, so as to obtain all the related relationships between concepts.

Match the dependencies between phrases and words and between phrases according to the preset word structure, and identify the aggregation relationship, cardinality relationship and attribute relationship between concepts.

Specific implementations of the identification of the aggregation relationship may include:

For word structures such as "contain", "include", "type of", etc., as well as the possessive form of nouns, aggregate relationships are expressed. Taking the sentence "A contains B" or "A's B" as an example, an aggregation relationship in which the source concept is B and the target concept is A can be extracted.

Specific implementations of the identification of the cardinality relationship may include:

The singular and plural forms of indefinite articles, ordinal numbers, and nouns in word structure represent cardinality relationships.

A relationship is a one-to-one relationship if its source and target concepts are both singular.

A relationship is a many-to-many relationship if its source and target concepts are both plural.

If the source concept of an association relationship is singular and the target concept is plural, the relationship is a one-to-many relationship.

If the source concept of an association relationship is plural and the target concept is singular, the relationship is a many-to-one relationship.

If the source concept or target concept of an association relationship is preceded by an explicit number, the number indicates a cardinality relationship.

Specific implementations of attribute relationship identification may include:

Word structures such as "identified by", "recognized by", etc. can represent attributes. Taking the sentence "A isidentified by B" as an example, it can be extracted that B is an attribute of concept A.

The adjectives that modify the concept represent the attributes of the concept, which are reflected in the attributive or phylogenetic structure in natural language. The attribute expresses the attribute of the noun phrase it modifies, and the predicate expresses the attribute of the subject.

Intransitive verbs with adverb or complement modifiers denote attributes. Taking the sentence "The train arrives in the morning at 10am." as an example, it can be inferred from the intransitive verb "arrives" and the complement that follows that the concept "train" should have an attribute "arrival time".

After obtaining the semantic relationship between concepts, the aggregation relationship, cardinality relationship and attribute relationship can be further distinguished, thereby improving the recognition accuracy of the three.

Optionally, the corresponding domain model is determined according to the association relationship between the concepts, including:

Traversing the boundary concepts in the association relationship between the concepts;

Modifying the association relationship of the boundary concepts that match the preset fields in the boundary concepts;

Wherein, the boundary concept is that there is only one other concept that has a semantic relationship with the boundary concept.

The boundary concept in this embodiment means that if there is one and only one other concept that has a relationship with this concept, this concept is called a boundary concept. For the semantic relationship between the obtained concepts that contains the boundary concept, check all the associations that contain the boundary concept, if the content of the association can match the structure of "include in", "including", "consist of", etc., then the association The relationship is modified to an aggregate relationship or attribute. For example, the results can be extracted based on the existing domain model, and all boundary concepts of the domain model and the associations connecting the boundary concepts can be checked. If the specific content of this relationship can match the synonyms of the schema representing the aggregation relationship, such as "contain", "include", etc., then modify the relationship to an aggregation relationship or attribute.

Compared with the extraction results of domain modeling experts, the method of the present invention can extract 95% of the relationships in the requirement document.

In conclusion, the method of the present invention expands the extraction rules of the domain model, introduces a variety of new dependencies and grammatical structures for extracting the domain model, and can extract the information represented by the prepositional phrase structure and the complement structure more comprehensively and accurately. The method of the invention also proposes the boundary concept in the domain model, and proposes a method for checking the association relationship including the boundary concept, which can improve the accuracy of the association relationship, the aggregation relationship and the attribute identification.

Embodiment 2

The second embodiment of the present invention provides a domain model extraction device, as shown in FIG. 3 , including:

The analysis unit is used to syntactically analyze the requirement document and determine the dependency between the word segmentations;

a relationship determining unit, configured to determine the semantic relationship between concepts according to the dependency between the word segments;

A domain model determining unit, configured to determine a corresponding domain model according to the semantic relationship between the concepts.

In the embodiment of the present invention, the dependency relationship between word segments in the requirement document is determined; the semantic relationship between concepts is determined according to the dependency relationship between the word segments; the corresponding domain model is determined according to the semantic relationship between the concepts, thereby The extraction accuracy of the domain model is improved.

Embodiment 3

A third embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored thereon, and when the computer program is executed by a processor, implements the steps of the domain model extraction method of the first embodiment.

In an optional embodiment, when the computer program is executed by a processor, it realizes:

Perform grammatical analysis on the requirements document to determine the dependencies between the word segmentations;

Determine the semantic relationship between concepts according to the dependency relationship between the word segmentations;

The corresponding domain model is determined according to the semantic relationship between the concepts.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.

From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course hardware can also be used, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present invention.

The embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are merely illustrative rather than restrictive. Under the inspiration of the present invention, without departing from the scope of protection of the present invention and the claims, many forms can be made, which all belong to the protection of the present invention.

Claims (5)

1. a domain model extraction method, is characterized in that, comprises: Perform grammatical analysis on the requirements document to determine the dependencies between the word segmentations; Determine the semantic relationship between concepts according to the dependency relationship between the word segmentations; Determine the corresponding domain model according to the semantic relationship between the concepts; Determine the semantic relationship between concepts according to the dependencies between the word segments, including: Traverse the noun phrases in the participle, and determine the dependencies between phrases and words and between phrases; Extract semantic relationships between concepts based on dependencies between phrases and words and between phrases; Traverse the noun phrases in the participle, the dependencies between derived phrases and words, and between phrases, including: If the target node corresponding to the dependency relationship with the word in the current noun phrase as the source node falls into the current noun phrase, the current noun phrase is not derived; If the target node corresponding to the dependency with the word in the current noun phrase as the source node falls outside the current noun phrase, the current noun phrase is derived; Derive the current noun phrase, including: if the derived word is a source node word in a noun phrase other than the current noun phrase, then derive the dependency between the phrases, otherwise derive the dependency between the phrase and the word relation; Extract semantic relationships between concepts based on dependencies between phrases and words and between phrases, including: Extract the association relationship between concepts according to the source nodes corresponding to different grammatical structures according to the dependencies between phrases and words and between phrases; and, Match the dependencies between phrases and words and between phrases according to the preset word structure, and identify the aggregation relationship, cardinality relationship and attribute relationship between concepts; Determine the corresponding domain model according to the association relationship, including: Traversing the boundary concepts in the association relationship between the concepts; Modifying the association relationship of the boundary concepts that match the preset fields in the boundary concepts; Wherein, the boundary concept is that there is only one other concept that has a semantic relationship with the boundary concept. 2. The method for extracting a domain model according to claim 1, wherein the requirement document is grammatically analyzed, comprising: Decompose the requirement document to obtain the corresponding word segmentation; Perform part-of-speech tagging based on the part-of-speech, and determine the corresponding part-of-speech type according to the part-of-speech tagging result; Based on the word segmentation type, a dependency relationship between each word segmentation is determined. 3. The domain model extraction method as claimed in claim 2, characterized in that, after determining the corresponding word segmentation type according to the part-of-speech tagging result, further comprising: cleaning the participle; Extract the word stems in the cleaning result; A reduction process is performed on the participle stems. 4. A domain model extraction device, characterized in that, comprising: The analysis unit is used to syntactically analyze the requirement document and determine the dependency between the word segmentations; a relationship determining unit, configured to determine the semantic relationship between concepts according to the dependency between the word segments; a domain model determination unit, configured to determine a corresponding domain model according to the semantic relationship between the concepts; Determine the semantic relationship between concepts according to the dependencies between the word segments, including: Traverse the noun phrases in the participle, and determine the dependencies between phrases and words and between phrases; Extract semantic relationships between concepts based on dependencies between phrases and words and between phrases; Traverse the noun phrases in the participle, the dependencies between derived phrases and words, and between phrases, including: If the target node corresponding to the dependency relationship with the word in the current noun phrase as the source node falls into the current noun phrase, the current noun phrase is not derived; If the target node corresponding to the dependency relationship with the word in the current noun phrase as the source node falls outside the current noun phrase, the current noun phrase is derived; Derivating the current noun phrase, including: if the derived word is a source node word in a noun phrase other than the current noun phrase, then deriving to obtain the dependency relationship between the phrases, otherwise deriving to obtain the relationship between the phrase and the word dependencies; Extract semantic relationships between concepts based on dependencies between phrases and words and between phrases, including: Extract the association relationship between concepts according to the source nodes corresponding to different grammatical structures according to the dependencies between phrases and words and between phrases; and, Match the dependencies between phrases and words and between phrases according to the preset word structure, and identify the aggregation relationship, cardinality relationship and attribute relationship between concepts; Determine the corresponding domain model according to the association relationship, including: Traversing the boundary concepts in the association relationship between the concepts; Modifying the association relationship of the boundary concepts that match the preset fields in the boundary concepts; Wherein, the boundary concept is that there is only one other concept that has a semantic relationship with the boundary concept. 5. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is realized. steps of the method.

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