CN112732251A - Semi-automatic generation method of service value network facing service internet - Google Patents
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Abstract
The invention discloses a semi-automatic generation method of a service value network facing to a service internet. Step 1: constructing an abstract layer value network according to prior knowledge; the abstract layer value network comprises a huge head node, other service participants and a value exchange relation among the huge head node, the other service participants and the abstract layer value network; step 2: automatically generating nodes, namely specific examples of service participants, by calculation according to information in HTML files in the huge head node website; and step 3: the value exchange relationship between the nodes generated in the step 2 and the corresponding values thereof; and 4, step 4: carrying out artificial correction on the specific examples and the value exchange relationship of the service participants obtained in the step 2 and the step 3; and 5: and finally generating an example level value network based on the value exchange relationship artificially corrected in the step 4. The efficiency of obtaining the service value is improved, specific analysis methods aiming at the service value are provided, and corresponding optimization and innovation schemes are provided according to defects and deficiencies in the value level of the whole service system.
Description
Technical Field
The invention belongs to the field of service internet; in particular to a semi-automatic generation method of a service value network facing to a service internet.
Background
With the rapid development of the service industry nowadays, more and more new technologies are applied to the service industry. To help enterprises build better quality services, new technologies for many service-related applications emerge. Especially on a service system, this is a complex system, not only the "sum" of its internal constituent elements, but also the complex interaction between them. Such complex interactions between elements are difficult to predict and then relatively difficult in the modeling phase.
To solve this problem, there are various service modeling methods, but most of the methods are not focused on the service value. With the progress of research, the service engineering which comes with the progress pays more attention to the service value and the value interaction process. Service value is sought by all service participants in a particular service interaction process, and the realization of the value should also be used as a core part of the system in the service system. Based on the importance of service value in the whole service engineering, the research on the service value is particularly important. However, most of the existing service systems do not model and analyze the value of the service in an overall and detailed way.
The value chain and the value network are created for describing how the value is through complex dynamic interaction among multiple parties, and are used for improving enterprise competitiveness after the enterprise service process is better understood and optimized, but only the value chain and the value network can qualitatively describe the value in the service from a conceptual level and lack detailed description of value attributes, so that a value system in the whole service internet needs to be more perfect.
Disclosure of Invention
The invention provides service internet-oriented related field knowledge, and aims to solve the technical problems in the background art.
The invention is realized by the following technical scheme:
a semi-automatic generation method of a service value network facing to a service Internet comprises the following steps:
step 1: constructing an abstract layer value network according to prior knowledge; the abstract layer value network comprises a huge head node, other service participants and a value exchange relation among the huge head node, the other service participants and the abstract layer value network;
step 2: automatically generating nodes, namely specific examples of service participants, by calculation according to information in HTML files in the huge head node website;
and step 3: the value exchange relationship between the nodes generated in the step 2 and the corresponding values thereof;
and 4, step 4: carrying out artificial correction on the specific examples and the value exchange relationship of the service participants obtained in the step 2 and the step 3;
and 5: and finally generating an example level value network based on the value exchange relationship artificially corrected in the step 4.
Further, the step 1 specifically includes firstly carrying out value modeling on the value, and then carrying out subsequent modeling according to the value modeling; wherein the value base concept extension comprises a value definition, a value classification and a value network model definition.
Further, the step 2 is specifically to classify the specific examples of the service participants, establish output nodes, manually confirm the output nodes by using a tool, and modify and delete the added nodes;
automatically identifying participants in a service value network facing to a service internet in a plurality of related HTML webpage files, classifying and outputting types of five service participants, namely a customer C, a customer enabler CE, a platform B, a provider enabler PE and a provider P;
selecting five aspects of information in the HTML file as vectorization and classification directions during classification, wherein the five aspects comprise entity text information, a structure position in a webpage, a current webpage title, current webpage keywords and a context of an entity in the webpage;
and vectorizing by adopting Bert, and classifying the types of the participants by combining a lightGBM lifting tree method so as to establish the model service participants and the categories thereof.
Further, the step of automatically identifying the participants in the service value network facing the service internet specifically includes extracting the participants and entities of the value by using Bert + BilSTM + CRF, generating a participant-value-participant relationship according to an artificially defined extraction rule, and judging whether the value is the participant or not and which type of value is the participant by using lightGBM.
Further, the step 3 is specifically to perform vectorization by using berts as service through the input news forecast, and includes the following steps:
step 3.1: carrying out BIO labeling on the preprocessed news forecast;
step 3.2: training a model by using BilSTM + CRF according to vectorized data;
step 3.3: carrying out output classification on the model trained in the step 3.2 to obtain a corresponding entity;
step 3.4: and extracting the relationship according to the rule to generate a value exchange relationship.
Further, the step 4 is specifically to correct and generate the value network, and after the value network is output to the display board after automation, a user can manually supplement or correct the value information by using a tool; manually confirming whether the value is the value or not and what type of value is the value, so as to ensure the accuracy of the construction of the value network; and matching the extraction-only value providing algorithm with the expected value and the transfer type of the abstract layer to generate a final value network.
The invention has the beneficial effects that:
the invention improves the efficiency of obtaining the service value, provides some specific analysis methods aiming at the service value, and provides corresponding optimization and innovation schemes according to some defects and deficiencies in the value level in the whole service system. Therefore, the requirements of users are met, service participants can guarantee maximization of service value in service, and the satisfaction degree of the service participants on the service is improved.
The invention greatly expands the sources of the value exchange relation acquisition through news corpora such as service events on the website. And natural language processing and machine learning means are introduced for processing.
Compared with the universal automatic and semi-automatic modeling tools, the method has the advantage that the knowledge in the field of value modeling is included in algorithm design.
The value is constructed by automatically extracting basic texts, and compared with the normal manual construction of a value network, the tool and the method can greatly shorten the time and improve the modeling efficiency. And the method provides a concise modeling interface for a user to construct the value network by combining the display of the automatically generated nodes and edges, and supports the manual correction of the user to generate an accurate value network.
Drawings
FIG. 1 is a flow chart of the process of the present invention.
FIG. 2 is a schematic diagram of the modeling tool of the present invention.
FIG. 3 illustrates an example of a primitive and model according to the present invention.
FIG. 4 is a schematic illustration of the tool main interface of the present invention.
FIG. 5 is a flow chart of the present invention for extracting a value relationship model.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 to 5, a semi-automatic generation method of a service value network facing a service internet, the semi-automatic generation method comprising the steps of:
step 1: constructing an abstract layer value network according to prior knowledge; the abstract layer value network comprises a huge head node, other service participants and a value exchange relation among the huge head node, the other service participants and the abstract layer value network;
step 2: automatically generating nodes, namely specific examples of service participants, by calculation according to information in HTML files in the huge head node website;
and step 3: the value exchange relationship between the nodes generated in the step 2 and the corresponding values thereof;
and 4, step 4: carrying out artificial correction on the specific examples and the value exchange relationship of the service participants obtained in the step 2 and the step 3;
and 5: and finally generating an example level value network based on the value exchange relationship artificially corrected in the step 4.
Furthermore, the invention introduces the extension of value definition, namely a value network modeling tool for modeling the value exchange relationship by the value expansion definition; the step 1 is specifically that value modeling is carried out on the value, and subsequent modeling is carried out according to the value modeling; wherein the value base concept extension comprises a value definition, a value classification and a value network model definition.
Furthermore, in the semi-automatic generation method of the service value network facing the service internet, the value network is generated according to the defined abstract layer value network in a certain field; the step 2 is to classify the specific examples of the service participants, establish output nodes, manually confirm the output nodes by using a tool, and modify and delete the added nodes;
automatically identifying participants in a service value network facing to a service internet in a plurality of related HTML webpage files, classifying and outputting types of five service participants, namely a customer C, a customer enabler CE, a platform B, a provider enabler PE and a provider P; thereby generating basic information for the nodes serving the participant entities in the value network. When there is no direct value exchange relationship of the service participant entities, based on the exchange relationship of the types of the service participants in the abstract layer value network, the expected and actually provided values of the different service participants and the corresponding service participants in the text are extracted subsequently, so as to achieve the matching of the expected and actually provided values of the different types of service participant entities, and generate the corresponding value exchange relationship
Selecting five aspects of information in the HTML file as vectorization and classification directions during classification, wherein the five aspects comprise entity text information, a structure position in a webpage, a current webpage title, current webpage keywords and a context of an entity in the webpage;
and vectorizing by adopting Bert, and classifying the types of the participants by combining a lightGBM lifting tree method so as to establish the model service participants and the categories thereof.
Further, the step of automatically identifying the participant in the service value network facing the service internet specifically includes extracting the participant and the entity of the value by using Bert + BilSTM + CRF, generating the relationship between the participant and the value-participant according to an artificially defined extraction rule (i.e., a defined rule), and determining whether the value is the participant and which type of value is the participant by using lightGBM.
Further, the step 3 is specifically to perform vectorization by using berts as service through the input news forecast, and includes the following steps:
step 3.1: carrying out BIO labeling on the preprocessed news forecast;
step 3.2: training a model by using BilSTM + CRF according to vectorized data;
step 3.3: carrying out output classification on the model trained in the step 3.2 to obtain a corresponding entity;
step 3.4: and extracting the relationship according to the rule to generate a value exchange relationship.
Further, the step 4 is specifically to correct and generate the value network, and after the value network is output to the display board after automation, a user can manually supplement or correct the value information by using a tool; manually confirming whether the value is the value or not and what type of value is the value, so as to ensure the accuracy of the construction of the value network; and matching the extraction-only value providing algorithm with the expected value and the transfer type of the abstract layer to generate a final value network. The resulting value net is shown in figure 3.
Further, the modeling of the value network can be carried out by four steps of automatically generating nodes, generating nodes and providing values (unilateral value providing relationship), generating the value network and outputting the value network.
Further, the modeling of the value network can be carried out through three steps of automatically generating nodes, providing values (bidirectional value exchange relationship) and outputting the value network.
A method of natural language processing and machine learning is introduced, and a modeling method for a service internet value network is oriented. A value network model that characterizes the value exchange relationships based on the service internet concept can be generated. The method extracts entities and relations from the news corpus and related websites of the huge head nodes, helps a user to model a value network in a semi-automatic manner, and enables the user to finally analyze the value exchange relation between enterprises.
In the aspect of value network modeling, a plurality of models of natural language processing and machine learning are adopted, wherein a method for automatically extracting value relations from texts is used for automatically extracting entities which extract participants and values by using a Bert + BilSTM + CRF model aiming at news texts, and the relationship of the participants, the value and the participants is generated according to a well-defined rule. And the lightGBM is used for judging whether the value is the value and which specific value is the value, the Boosting method based on the decision tree can relatively well express under the condition of the small training set, and a user is helped to model the existing value exchange relationship so as to generate a value network. The front end of the tool is based on a Bootstrap frame, and the tool is very simple and visual. The interface is developed by adopting flash, and the interaction between the front end and the back end is realized.
Example 2
1. Constructing an abstract layer value web
The method is mainly characterized in that an established abstract value network is stored in the method, wherein the abstract value network comprises the large head nodes, other service participants and value exchange relations among the large head nodes and the other service participants. Firstly, modeling needs to be carried out aiming at the value, and then the subsequent modeling stage is carried out. Basic concept extensions to value for service-oriented internet include value definitions, value classifications and value net model definitions.
1.1 definition of value
Based on the traditional definition of value, the invention introduces the concept of a value realization carrier according to the state improvement or degree improvement of a certain aspect of a value receiver. Each value corresponds to its own value realization bearer, and the value is measured by state transition of the initial state and the end state of the value realization bearer. The state transition of the carrier brings revenue to the value receiver, and the revenue is determined by the realization values of a plurality of performance parameters. Here we formalize the definition values as follows:
a multi-dimensional tuple (vID, vName, Producer, Receiver, rc, rc.si, rc.so, VType), wherein:
vID, vName representing the unique identification, name, respectively, of the service value;
producer, Receiver representing value Producer, value Receiver, respectively;
VType represents the type of value itself, including the ten classes mentioned later;
rc, rc.si, rc.so are the initial state of the value realization carrier, rc, respectively, the expected end state of rc;
1.2 Classification of values
Based on the existing value definitions and classifications, the present invention refines the classification of values as shown in Table 1:
TABLE 1 service value definition and Classification
1.3 value net model
The participant-oriented value network introduces all value participants, forming a value network between customers, customer providers, intermediate platforms, providers, and provider enablers, as shown in FIG. 3. The service internet oriented value network model is defined as follows:
SVN ═ is (Actors, Values, recipiens), defining the value network as triplets, where Actor and recipien are service participants and Values are the value passed in the value network.
Service participant Actor (TSPIndicator)
AName indicates the name of participant A in the service, and AType ∈ { Customer, Provider, Customer Enabler, Provider Enabler, Broker } indicates the type of participant A.
Service Value (VID, VName, VType,)
The VID, the Vname and the VType represent basic information of the service value, and the basic information comprises a unique identification vID of the service value, a name vName and a service value type.
2. Method for extracting participant entity
The method mainly provides a semi-automatic algorithm to classify the service participant instances according to the html files of the input huge head related websites, outputs nodes, uses tools to carry out manual confirmation, and can modify, delete and add the nodes. Vectorization herein is performed using Bert and the method of lightGBM lifting tree is combined to classify the types of participants, helping to model the service participants and their categories.
Aiming at the types of service participants, the service-oriented internet project mainly models a service value network with a huge head node as a center, and five service participants mainly exist in the service-oriented internet value network, wherein specific definitions, possible expected value types and possible provided value types are shown in table 2.
TABLE 2 classification of service participants
According to the defined value network of an abstract layer in a certain field, the invention provides an extraction classification method, which automatically identifies participants in the value network in a plurality of related HTML webpage files and outputs the types (C, CE, B, PE and P) of the participants. Wherein, the selection of five aspects of the information in the html file as the vectorization and classification directions is proposed during classification, which comprises the following steps: the text information of the entity, the structure position in the webpage, the title of the current webpage, the keywords of the current webpage and the context of the entity in the webpage. Vectorization herein is performed using Bert and the method of lightGBM lifting tree is combined to classify the types of participants, helping to model the service participants and their categories.
3. Method for extracting value exchange relationship
The method mainly provides an automatic algorithm aiming at a specific service participant example, and realizes three different functions by combining different input text types:
(1) and sentences with valuable exchange relations, such as Actor-value-Actor.
(2) And aiming at the value-related text of the specific example, the type is such as Actor-value.
(3) The business scope for a particular instance specifies a format input, of the type { actor: text: }
A method for automatically extracting value relations from texts is mainly adopted, and for news text automatic extraction, a Bert + BilSTM + CRF model is used for extracting participants and entities of values, and participant-value-participant relations are generated according to defined rules, wherein the relationships are named entity identification problems and relation extraction problems processed by natural language. As shown in the flow chart of fig. 5, vectorization is performed by adopting berts as service through input news forecast, BIO labeling is performed on the preprocessed news forecast, then a BilSTM + CRF training model is adopted according to vectorized data, and then output classification is performed to obtain a corresponding entity. And then extracting the relationship according to the rule of the manually defined relationship to generate a plurality of complete value exchange relationships.
4. Correction generating value net
The step mainly carries out artificial correction on the specific examples and the value exchange relation of the service participants obtained in the step. After the value information is automatically output to the display board, the user can manually supplement and correct the value information by a tool. And manually confirming whether the value is the value or not, what type of value is the value and the like so as to ensure the accuracy of the construction of the value network. And matching the extraction-only value-providing algorithm with the expected value and the transfer type of the abstract layer to generate a final value network, wherein the final value network is shown in FIG. 3.
5. Semi-automatic modeling tool of value net
On the basis of the method provided by the invention, and as shown in the figure 2, the invention provides a semi-automatic modeling tool of a value network, and provides a more efficient modeling method for users. The selection of the two methods is provided, so that a user can select the modeling method according to the specific semantic information of the text. The method comprises the following steps of automatically generating nodes, generating nodes and providing values (unilateral value providing relationship), generating a value network and outputting the value network; and the second method adopts three steps of automatically generating nodes, providing value (bidirectional value exchange relationship) and outputting the value network to carry out modeling of the value network.
As shown in FIG. 4, the front end of the tool is based on a Bootstrap frame, which is very concise and intuitive. The interface is developed by adopting flash, and the front end and the back end are interacted. The back end mainly uses the modeling method for extracting the value based on the text to carry out modeling, and realizes a modeling method of a value network with strong operability and high automation degree.
Claims (6)
1. A semi-automatic generation method of a service value network facing to a service Internet is characterized by comprising the following steps:
step 1: constructing an abstract layer value network according to prior knowledge; the abstract layer value network comprises a huge head node, other service participants and a value exchange relation among the huge head node, the other service participants and the abstract layer value network;
step 2: automatically generating nodes, namely specific examples of service participants, by calculation according to information in HTML files in the huge head node website;
and step 3: the value exchange relationship between the nodes generated in the step 2 and the corresponding values thereof;
and 4, step 4: carrying out artificial correction on the specific examples and the value exchange relationship of the service participants obtained in the step 2 and the step 3;
and 5: and finally generating an example level value network based on the value exchange relationship artificially corrected in the step 4.
2. The semi-automatic generation method of the service value network oriented to the service internet as claimed in claim 1, wherein the step 1 is specifically that value modeling is performed on the value first, and then subsequent modeling is performed according to the value modeling; wherein the value base concept extension comprises a value definition, a value classification and a value network model definition.
3. The method for semi-automatically generating the service value network oriented to the service internet as claimed in claim 1, wherein the step 2 is to classify the specific examples of the service participants, establish output nodes, manually confirm the output nodes by using a tool, and modify and delete the added nodes;
automatically identifying participants in a service value network facing to a service internet in a plurality of related HTML webpage files, classifying and outputting types of five service participants, namely a customer C, a customer enabler CE, a platform B, a provider enabler PE and a provider P;
selecting five aspects of information in the HTML file as vectorization and classification directions during classification, wherein the five aspects comprise entity text information, a structure position in a webpage, a current webpage title, current webpage keywords and a context of an entity in the webpage;
and vectorizing by adopting Bert, and classifying the types of the participants by combining a lightGBM lifting tree method so as to establish the model service participants and the categories thereof.
4. The method of claim 3, wherein the automatic identification of the participants in the service value network is performed by extracting entities of the participants and the value according to Bert + BilsTM + CRF, generating the relationship between the participants and the value-participants according to manually defined extraction rules, and determining whether the participants are the value and which type of the value.
5. The semi-automatic generation method of the service value network facing the service internet as claimed in claim 1, wherein the step 3 is specifically vectorized by adopting a bertas service through the input news forecast, and comprises the following steps:
step 3.1: carrying out BIO labeling on the preprocessed news forecast;
step 3.2: training a model by using BilSTM + CRF according to vectorized data;
step 3.3: carrying out output classification on the model trained in the step 3.2 to obtain a corresponding entity;
step 3.4: and extracting the relationship according to the rule to generate a value exchange relationship.
6. The semi-automatic generation method of the service value network facing the service internet as claimed in claim 1, wherein the step 4 is specifically a correction generation value network, and after the value network is output to a display board after automation, a user can manually supplement or correct value information by using a tool; manually confirming whether the value is the value or not and what type of value is the value, so as to ensure the accuracy of the construction of the value network; and matching the extraction-only value providing algorithm with the expected value and the transfer type of the abstract layer to generate a final value network.
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