CN116433799A - Flow chart generation method and device based on semantic similarity and sub-graph matching - Google Patents
Flow chart generation method and device based on semantic similarity and sub-graph matching Download PDFInfo
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Abstract
The invention discloses a flow chart generation method based on semantic similarity and sub-graph matching, which comprises the following steps: acquiring a user demand document and an RPA project asset library document; calculating the semantic similarity of the user demand document and the RPA project asset library document, and obtaining a top-k sub-graph to be matched according to the semantic similarity from high to low; constructing a query knowledge graph according to a user demand document, and setting a starting node; searching and matching the subgraphs to be matched in the query knowledge graph to obtain a final optimal matching graph, and returning to the corresponding flow chart. The technical scheme of the invention reduces the time of sub-graph matching search traversal, and more accurately generates the flow chart of the current user requirement from dual constraint of semantics and structure.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a flow chart generation method and device based on semantic similarity and sub-graph matching.
Background
For execution of business processes, employees currently spend a great deal of time processing Enterprise Resource Planning (ERP), customer Relationship Management (CRM), spreadsheets, and legacy systems, performing manually repeatable tasks such as entering, copying, pasting, extracting, merging, and moving data of large volumes of data from one system to another. It is contemplated that some of these highly structured, routine, and manual tasks may be handled by robots, so that knowledge workers have more time to handle value-added tasks. Robotic Process Automation (RPA) emerges as a software-based solution for automating rule-based business processes that involve routine tasks, structured data, and deterministic results. The flow chart is an important ring in the RPA technology, the flow chart is drawn according to the requirement of a user, and then the execution code is generated according to the flow chart to complete the appointed operation. The flow chart is helpful in determining how things are going and deciding how the process should be improved. And the manual drawing of the flow chart requires longer time and consumes more manpower resources. How to automatically generate a flow chart for the current user demands by utilizing the existing flow charts in the RPA implementation library can save manpower and material resources, greatly improves the efficiency of RPA implementation, and is the direction of current research.
Disclosure of Invention
In view of this, the invention proposes a flow chart generating method based on semantic similarity and sub-graph matching, comprising the following steps:
s1, acquiring a user demand document and an RPA project asset library document;
s2, calculating semantic similarity of the user demand document and the RPA project asset library document, and obtaining a top-k sub-graph to be matched according to the semantic similarity from high to low;
s3, constructing a query knowledge graph according to the user demand document, and setting a starting node;
and S4, searching and matching the subgraphs to be matched in the query knowledge graph to obtain a final optimal matching graph, and returning to the corresponding flow chart.
The invention also provides a flow chart generating device based on semantic similarity and sub-graph matching, which comprises the following steps:
a processor;
a memory having stored thereon a computer program executable on the processor;
the method comprises the steps of generating a flow chart based on semantic similarity and sub-graph matching, wherein the computer program is executed by the processor.
The technical scheme provided by the invention has the beneficial effects that:
according to the technical scheme provided by the invention, the items similar to the current demands in the project library are screened out through semantic similarity by using the demand documents of the users, meanwhile, the demand documents of the users are constructed into small demand graphs, and the small demand graphs are matched with the RPA project asset knowledge graph by using a fuzzy sub-graph matching method, so that the items similar to the current user demand flow structure are found. First, semantic is used for preliminary screening, and then sub-graph matching is used for searching traversal. On one hand, the time for sub-graph matching search traversal is reduced, and on the other hand, the flow chart of the current user requirement is generated more accurately by double constraint from semantics and structure. The flow chart is automatically generated according to the user requirement by using the generation technology, so that the user can directly use or fine tune conveniently, the process of drawing the flow chart is simplified, the manual intervention is greatly reduced, and the RPA efficiency is improved.
Drawings
FIG. 1 is a flow chart of a flow chart generation method based on semantic similarity and sub-graph matching according to an embodiment of the present invention;
FIG. 2 is a knowledge graph of the agricultural online silver flow water downloading user requirements constructed in an embodiment of the invention;
fig. 3 is a diagram of the network silver flow water downloading knowledge of RPA asset library project B company constructed in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
The invention provides a flow chart generation method based on semantic similarity and sub-graph matching, referring to fig. 1, fig. 1 is a flow chart of a flow chart generation method based on semantic similarity and sub-graph matching, comprising the following steps:
s1, acquiring a user demand document and an RPA project asset library document.
In a further embodiment, the user's demand is a farm bank running water download.
And S2, calculating the semantic similarity of the user demand document and the RPA project asset library document, and obtaining a top-k sub-graph to be matched according to the semantic similarity from high to low.
The method comprises the following steps:
s21, a requirement document Q of a user and an RPA project asset library user documentAnd performing word segmentation and removing stop words.
In the embodiment, a word segmentation component jieba is used for a user requirement document Q and an RPA project asset library user documentAnd performing word segmentation.
S22, describing the document by using the frequency of word occurrence in the text, and enabling the user' S requirement document Q and RPA project asset library user documentRepresented as a one-dimensional vector.
In this embodiment, the method of normalizing the bag of words model of BOW is used to represent Q and Q, respectively。
S23, learning each word of the user demand document Q and the RPA project asset library user document according to the one-dimensional vector obtained in S22Is a term of similarity, in this vector space, the distance between semantically similar terms is similar.
In this embodiment, the word2vec method is used to learn the userEach word of requirement document Q and RPA project asset library user documentIs a term for each word.
S24, calculating user requirement document Q and RPA project asset library user document by using WMD algorithmThe text similarity between the two documents is obtained by the WMD algorithm, the document distance is modeled into a combination of semantic distances of words in the two documents, the Euclidean distance is calculated for word vectors corresponding to any two words in the two documents, and then the Euclidean distances are weighted and summed. The WMD algorithm is an algorithm that measures the similarity of texts by calculating the distance of words between texts based on word2 vec.
S241, document Q and documentThe number of times of word occurrence in the document Q is normalized, and word frequency +.>Document->The word frequency of the jth word in the list is +.>:
Wherein m and n are respectively the document Q and the document D s The number of words in the medium-sized word,、/>respectively represent document Q and document D s The number of occurrences of the ith word and the jth word;
s242, calculating word i from document Q and word D from document D s The Euclidean distance between two words of word j of (a) is:
s243, solving the document Q and the RPA project asset library D by using a dynamic programming algorithm s WMD distance of each document:
wherein ,representing the mapping of word i in document Q to document D s Weights of word j in +.>Representing word i and document D in document Q s Distance between words j in (a); notably, the ith word of document Q corresponds to D s Weights of all words in a documentThe sum of the values is equal to->Similarly, D s The sum of the weight values of all words of the j-th word of the document mapped to document Q is equal to +.>Wherein the smaller the f-number, the more similar the two documents.
S244, calculating document Q and document D s Similarity of (3):
s245, setting a similarity thresholdAccording to document Q and document D s The similarity of (2) is less than the threshold +.>And (3) the k RPA project asset documents and the knowledge maps and flowcharts corresponding to the k projects.
In a further embodiment, referring to fig. 2 and fig. 3, fig. 2 is a rural online banking water downloading user demand knowledge graph constructed in the embodiment of the present invention, and fig. 3 is an RPA asset library project B company online banking water downloading knowledge graph constructed in the embodiment of the present invention. The user's demand documents include farm bank running water, etc., and project documents that can be matched to similar completions in the RPA asset library, such as company B running water, including construction bank running water and industry bank running water downloads.
S3, constructing a query knowledge graph according to the user demand document, and setting a starting node. As shown in fig. 2, the user's demand is a rural internet banking serial download, and includes the substeps of a rural internet banking U-shield login, a rural internet banking serial export, and a rural internet banking serial data conversion.
And S4, searching and matching the subgraphs to be matched in the query knowledge graph to obtain a final optimal matching graph, and returning to the corresponding flow chart.
S41, searching the best possible matching result of the query knowledge graph in the sub-graph to be matched by using a TALE approximate large graph matching tool.
S42, if the best matching result is searched in the step S41, returning the matched candidate subgraph and the flow chart corresponding to the matched candidate subgraph, and if the best matching result is not searched, executing the step S43.
S43, dividing the graph according to the attribute of the edge of the current starting node as the inclusion relation to obtain a sub-graph set, and repeatedly executing the step S41 and the step S42, wherein the starting node is updated as the tail entity of the current node until the best matching result is obtained or the sub-graph set is empty.
The invention also provides a flow chart generating device based on semantic similarity and sub-graph matching, which comprises the following steps:
a processor;
a memory having stored thereon a computer program executable on the processor;
the method comprises the steps of generating a flow chart based on semantic similarity and sub-graph matching, wherein the flow chart is generated by the computer program when the computer program is executed by the processor.
According to the technical scheme provided by the invention, the similarity between the user demand document and the RPA project asset library document is measured by using a WMD algorithm, and the project similar to the current user demand is found, so that the scope of further searching by using sub-graph matching is reduced, and the efficiency is improved.
The method of matching the graph division with the subgraph is combined, and the optimal matching subgraph is searched iteratively. Searching the best matching subgraph from the RPA project asset knowledge graph by using a fuzzy subgraph matching method, allowing certain nodes to be unmatched and certain edges to be missed by fuzzy subgraph matching, recommending a related flow chart, and manually participating in correcting the flow chart. When the searching is not completed, the concept of edge division of graph division is utilized, the characteristics of the knowledge graph required by the user are considered at the same time, the relationship edge is limited to be 'containing' relationship, sub-graph division is carried out, and then the best matching sub-graph is searched.
The method for matching the semantic similarity and the subgraph is utilized to jointly generate a flow chart: firstly, searching for the first round by using semantic similarity to obtain a history item which is more similar to the user demand, and simultaneously reducing the matching search space of the second wheel diagram; and secondly, carrying out a second round of search by using fuzzy subgraph matching, finding a history item which is similar to the user demand semanteme and structure as an optimal matching item, and finding a corresponding flow chart.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The flow chart generation method based on semantic similarity and sub-graph matching is characterized by comprising the following steps of:
s1, acquiring a user demand document and an RPA project asset library document;
s2, calculating semantic similarity of the user demand document and the RPA project asset library document, and obtaining a top-k sub-graph to be matched according to the semantic similarity from high to low;
s3, constructing a query knowledge graph according to the user demand document, and setting a starting node;
and S4, searching and matching the subgraphs to be matched in the query knowledge graph to obtain a final optimal matching graph, and returning to the corresponding flow chart.
2. The flow chart generating method based on semantic similarity and sub-graph matching according to claim 1, wherein step S2 specifically comprises:
s21, a requirement document Q of a user and an RPA project asset library user documentThe word segmentation is carried out,and removing stop words;
s22, describing the document by using the frequency of word occurrence in the text, and enabling the user' S requirement document Q and RPA project asset library user documentRepresented as a one-dimensional vector;
s23, learning each word of the user demand document Q and the RPA project asset library user document according to the one-dimensional vector obtained in S22Is a term for each word;
6. The method for generating a flow chart based on semantic similarity and sub-graph matching according to claim 2, wherein step S24 specifically comprises:
s241, document Q and documentThe number of times of word occurrence in the document Q is normalized, and word frequency +.>Document->The word frequency of the jth word in the list is +.>:
Wherein m and n are respectively the document Q and the document D s The number of words in the medium-sized word,、/>respectively represent document Q and document D s The number of occurrences of the ith word and the jth word;
s242, calculating word i and word ii from document QFrom document D s The Euclidean distance between two words of word j of (2) is C i,j :
s243, solving the document Q and the document D by using a dynamic programming algorithm s WMD distance of (b):
wherein f is document Q and document D s Is used for the distance of the WMD of (c),representing the mapping of word i in document Q to document D s Weights of word j in +.>Representing word i and document D in document Q s Distance between words j in (a); notably, the ith word of document Q corresponds to D s The sum of the weight values of all words in a document is equal to +.>Similarly, D s The sum of the weight values of all words of the j-th word of the document mapped to document Q is equal to +.>;
S244, calculating document Q and document D s Similarity of (3):
7. The flow chart generating method based on semantic similarity and sub-graph matching according to claim 1, wherein step S4 specifically comprises:
s41, searching the best possible matching result of the query knowledge graph in the sub-graph to be matched by using a TALE approximate large graph matching tool;
s42, if the best matching result is searched, returning the matched candidate subgraph and the flow chart corresponding to the matched candidate subgraph in the step S41, and if the best matching result is not searched, executing the step S43;
s43, dividing the graph according to the attribute of the edge of the current starting node as the inclusion relation to obtain a sub-graph set, and repeatedly executing the step S41 and the step S42, wherein the starting node is updated as the tail entity of the current node until the best matching result is obtained or the sub-graph set is empty.
8. A flow chart generation apparatus based on semantic similarity and sub-graph matching, the apparatus comprising:
a processor;
a memory having stored thereon a computer program executable on the processor;
wherein the computer program when executed by the processor implements a method of generating a flow chart based on semantic similarity and sub-graph matching as claimed in any one of claims 1 to 7.
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