CN115658918A - Address knowledge graph construction system and method based on Elasticissearch index and four-segment code - Google Patents
Address knowledge graph construction system and method based on Elasticissearch index and four-segment code Download PDFInfo
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Abstract
The invention relates to an address knowledge graph construction system based on an Elasticsearch index and four-segment codes, which comprises an Airflow task scheduling module, a data analysis module, a data association module, a keyword screening and voting module, an address graph construction module and a database, wherein standard four-level street data are stored in the database; the data analysis module obtains signing data of the express delivery and analyzes four-segment codes and POI keyword data; the data association module associates the analyzed data with corresponding standard four-level street data; the keyword screening and voting module screens and filters data with the score smaller than a preset numerical value in the associated data; the address map building module adds the associated data into an Elasticissearch index through four fields of province, city, district and POI, and builds an address knowledge map through screening voting data; also relates to a construction method of the address knowledge graph. The system and the method of the invention solve the problems of error, ambiguity and imperfection of the user address in the prior art and ensure the accuracy of map search.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to an address knowledge graph construction system and method based on an elastic search index and four-segment codes.
Background
The modern logistics industry is developing rapidly, but with the progress of the logistics industry, the industry also faces a lot of challenges. For example, when the original address is input, the address is wrong, the address contains a plurality of areas (double addresses), so that the address target is not clear, and the difference of administrative divisions of different upstream platforms and the change of the administrative divisions are not updated in time.
In order to improve the application of artificial intelligence in the logistics industry, the field largely uses knowledge graphs to carry out intelligent inference, and the use of the knowledge graphs is trained based on the existing POI data. Specifically, the address map system is composed of a national standard administrative division and a four-segment code (POI), solves the four-level administrative division recognition of the user address, and performs error correction (double addresses), completion and structuring processing on the user address. The trained knowledge map and other artificial intelligence technologies can simultaneously have the functions of address association, POI prediction and the like, address information is standardized and perfected, and the problems of delivery error, delivery delay, fuzzy logistics destinations and the like are fundamentally solved, so that the delivery efficiency is improved.
However, in the prior art, when an address knowledge graph is constructed and pushed, the problems of inaccurate pushed data and large deviation exist, so that the application of artificial intelligence in practice is doubted, and the address knowledge graph is analyzed and tried in detail. We develop an address knowledge graph construction system and method based on the Elasticissearch index and four-segment code, which are used for solving the problems of error, ambiguity and imperfection of the user address in the prior art.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an address knowledge graph construction system and method based on an elastic search index and four-segment codes. The address map construction system and the construction method of the invention have the advantages that the memory can be saved during searching, the performance is high, and the constructed address map is more perfect and accurate based on rich four-segment code data
In order to achieve the purpose of the invention, the invention provides the following technical scheme:
the invention firstly designs an address knowledge graph construction system based on an Elasticsearch index and four-segment codes, which comprises an Airflow task scheduling module, a data analysis module, a data association module, a keyword screening and voting module, an address graph construction module and a database, wherein,
the Airflow task scheduling module is started to realize the management and scheduling of the address map construction task when the map construction or the online data change needs to be reconstructed;
the database obtains administrative division data from an official statistical department and obtains standard four-level street data;
the data analysis module obtains signing data of the express, the signing data comprises or writes back address data of a standard four-level street, and four-segment codes and POI keyword data in the signing data are analyzed;
the data association module is used for associating the analyzed four-segment codes and the POI keyword data to the corresponding standard four-level street data;
the keyword screening and voting module is used for calculating the ratio of the occurrence frequency of each POI in a four-level street where the POI is located to the occurrence frequency of the POI in a city where the POI is located to obtain a calculated score, setting a preset value, and screening and filtering out data with the score smaller than the preset value in the associated data;
the address map building module is used for splicing and de-duplicating all four-segment code POI under each four-level street in a space mode, adding associated data into an Elasticissearch index in four fields of province, city, region and POI, setting the word segmentation mode of the POI field to be recognized in a space word segmentation mode, and adding all four-segment code POI into an Elasticissearch custom word bank to screen the voted address data to build an address knowledge map.
In the address knowledge graph construction system based on the Elasticissearch index and the four-segment code, the Airflow task scheduling module is used for invoking the data analysis module when being started, and the Airflow is an open-source tool and is a platform for managing, scheduling and monitoring the workflow in a programming mode.
In the invention, in the address knowledge graph construction system based on the Elasticissearch index and the four-segment code, the fact that the address data of the standard four-level street is written back in the signing data means that the signing data comprises a signing network point, and the standard four-level street data is written back according to the address back-push of the signing network point.
In the address knowledge graph construction system based on the elastic search index and the four-segment code, the POI data refers to the Point of Interest data, the POI is an abbreviation of 'Point of Interest', and Chinese can be translated into 'Point of Interest'. In the geographic information system, one POI may be one house, one shop, one mailbox, one bus station, and the like.
In the address knowledge graph construction system based on the Elasticsearch index and the four-segment code, the score calculation process of the POI in the four-level street in the keyword screening voting module is as follows:
the score of the POI on its level four street = the number of times the POI appears in its level four street/the number of times the POI appears in the city of its level four street 100.
In the address knowledge graph construction system based on the Elasticissearch index and the four-segment code, the keyword screening and voting module respectively keeps the graph test accuracy rate of the constructed graph with the score larger than a certain value, and the score with the highest overall accuracy rate is used as the score threshold value of the constructed graph keyword voting.
The invention also relates to an Address knowledge graph construction method based on the Elasticissearch index and the four-segment code, which comprises the following steps:
step 1, acquiring standard four-level street data, downloading administrative division data from a national statistical department, and importing the downloaded data into a special database;
step 2, analyzing the four-segment code and the POI data to obtain address data in the sign-in data, wherein the address data comprises or is written back to address data of a standard four-level street, and analyzing the data of the four-segment code and other POI keywords in the address data;
step 3, associating the four-segment codes and the POI to standard four-level street data, and associating the analyzed address data to the corresponding standard four-level street;
step 4, keyword screening and voting, namely calculating the ratio of the number of times each POI appears in the four-level street of the POI to the number of times each POI appears in the city where the four-level street of the POI is located, obtaining a score, filtering out the score in the associated data which is less than a certain numerical value, and obtaining address data after screening and voting;
and 5, constructing an address map, constructing the address map by using the screened and voted address data, splicing and de-duplicating all four-segment codes and POI fields under each four-level street in a space mode, adding the spliced data into an Elasticissearch index in four fields of province, city, district and POI, setting the word segmentation mode of the POI fields into space word segmentation, adding all the word-segmented four-segment codes and POI data into an Elasticissearch custom word library, constructing the address map by using the constructed new signing data, and searching by using an Elasticissearch engine to obtain the accurate four-level street.
And (3) the administrative division data downloaded in the step 1 are standard four-level street data, and the four-level street data are stored in the database.
And 4, filtering out the associated data with the score smaller than a certain numerical value, and obtaining the address data after the voting is screened, wherein the score smaller than the certain numerical value is not associated.
In step 5, an Elasticissearch search engine is used for searching and indexing in the constructed address knowledge graph, the constructed knowledge graph is that data is stored in the Elasticissearch, and the Elasticissearch is a search engine.
Compared with the prior art, the invention has the following technical advantages through practical application of the system and the method for constructing the address knowledge graph based on the Elasticissearch index and the four-segment code:
1. the knowledge map system and the construction method adopt the Elasticissearch and the four-segment code to realize an address map system, and the Elasticissearch is used for reversely indexing POI fields to construct a map; the search engine of the Elasticissearch depends on the bottom filesystem cache, if more memories are provided for the filesystem cache, the memories are basically used during searching, and the performance is very high; based on the rich four-segment code data, the constructed address map is more perfect and accurate, and the technical effect of the address map service with high performance and high accuracy is achieved.
2. The knowledge graph system and the construction method thereof use a keyword screening voting strategy, obtain scores with the highest accuracy through experiments and screen the scores during construction, and further solve the problem of poor search accuracy caused by the fact that original data contain wrong data or data with low relevancy.
Drawings
FIG. 1 is a schematic diagram of a composition architecture of an address knowledge graph construction system based on an Elasticissearch index and four-segment codes.
FIG. 2 is a schematic operation flow diagram of the address knowledge graph construction method based on the elastic search index and the four-segment code.
Fig. 3 is an illustration of an exemplary diagram of an Elasticsearch index in the method for constructing an address knowledge graph based on an Elasticsearch index and four-segment codes according to the present invention.
FIG. 4 is an experimental graph of optimal score threshold in the method for constructing an address knowledge graph based on the Elasticissearch index and four-segment code.
Detailed Description
The following will make a further detailed description of the address knowledge graph construction system and method based on the elastic search index and the four-segment code in order to clearly understand the architecture composition and workflow of the address knowledge graph, but not to limit the scope of the invention.
The address map system constructed by the invention is composed of national standard administrative divisions and four-segment codes (POI), solves the problem of four-level administrative division identification of the user address, performs error correction (double addresses), completion and structuralization processing on the user address, and simultaneously provides the functions of address association, POI prediction and the like. Address information is standardized and perfected, so that the problems of delivery errors, delivery delay, fuzzy logistics destinations and the like are fundamentally solved, and delivery efficiency is improved.
As shown in fig. 1, the invention firstly designs an address knowledge graph construction system based on an Elasticsearch index and four-segment codes, which comprises an Airflow task scheduling module, a data analysis module, a data association module, a keyword screening and voting module, an address graph construction module and a database, wherein,
the Airflow task scheduling module is started to realize the management and scheduling of the address map construction task when the map construction or the online data change needs to be reconstructed; the Airflow task scheduling module is used for scheduling the data analysis module when being started, and the Airflow is an open source tool and a platform for managing, scheduling and monitoring the workflow in a programming mode.
The database obtains administrative division data from an official statistical department and obtains standard four-level street data; the POI data refers to the data of Interest points, POI is the abbreviation of "Point of Interest", and Chinese can be translated into "Interest points". In the geographic information system, one POI may be a house, a shop, a mailbox, a bus station, etc.
The data analysis module obtains signing data of the express, the signing data comprises or writes back address data of a standard four-level street, and four-segment codes and POI keyword data in the signing data are analyzed; the fact that the address data of the standard four-level street are written back in the signing data means that the signing data comprise a signing point, and the data of the standard four-level street are written back according to the address of the signing point.
The data association module is used for associating the analyzed four-segment codes and the POI keyword data to the corresponding standard four-level street data;
the keyword screening and voting module is used for calculating the ratio of the occurrence frequency of each POI in the four-level street where the POI is located to the occurrence frequency of the POI in the city where the POI is located to obtain a calculated score, setting a preset numerical value and screening and filtering data of which the score is smaller than the preset numerical value in the associated data;
the address map building module is used for splicing and de-duplicating all four-segment codes and POI fields under each four-level street in a space mode, adding the associated data into an Elasticissearch index in four fields of province, city, district and POI, setting the word segmentation mode of the POI fields to be recognized in a space word segmentation mode, adding all four-segment codes POI into an Elasticissearch custom word bank, and screening the voted address data to build an address knowledge map.
In addition, because the search engine of the elastic search depends on the filesystem cache at the bottom layer, if more memories are provided for the filesystem cache, the memories are basically used during searching, the performance is very high, and the index advantage of the invention can be better embodied by combining with the endowment of more memories.
In the address knowledge graph construction system based on the Elasticsearch index and the four-segment code, the score calculation process of the POI in the four-level street of the POI in the keyword screening and voting module is as follows:
the score of the POI on its level four street = the number of times the POI appears in its level four street/the number of times the POI appears in the city of its level four street 100.
In the keyword screening and voting module, the scores with the highest overall accuracy are used as score threshold values for keyword voting of constructed maps by respectively keeping the accuracy of the constructed maps with scores larger than a certain value.
The invention also relates to an elastic search index and four-segment code based address knowledge graph construction method, which comprises the following steps:
step 1, acquiring standard four-level street data, downloading administrative division data from a national statistical department, and importing the downloaded data into a special database;
step 2, analyzing the four-segment code and the POI data to obtain address data in the sign-in data, wherein the address data comprises or writes back address data of a standard four-level street, and the data of the four-segment code and other POI keywords in the address data is analyzed;
step 3, associating the four-segment codes and the POI to standard four-level street data, and associating the analyzed address data to the corresponding standard four-level street;
step 4, keyword screening and voting, namely calculating the ratio of the number of times of each POI appearing in the fourth-level street of the POI to the number of times of the POI appearing in the city where the fourth-level street of the POI is located, obtaining a score, filtering out the score smaller than a certain numerical value in the associated data, and obtaining address data after screening and voting;
step 5, constructing an address map, constructing the address map by using the screened and voted address data, splicing and de-duplicating all four-segment code POIs under each four-level street in a space mode, adding the spliced data into an Elasticissearch index in four fields of province, city, district and POI, setting the word segmentation mode of the POI field as word segmentation in a space mode, adding all the four-segment codes after word segmentation and the POI data into an Elasticissearch custom word bank, constructing the address map by using the constructed new sign-in data, and searching by using an Elasticissearch engine to obtain the accurate four-level street.
Example 1
The implementation steps of the knowledge graph system constructed based on the Elasticsearch and the four-segment code in this embodiment are as follows:
step 1: a standard level four street is obtained. And downloading administrative division data from the national statistical bureau and importing the administrative division data into a database.
And 2, step: and analyzing the four-segment code and the POI data. 1) Acquiring address data of a standard four-level street, wherein the address of the sign-in data comprises or is written back; 2) Four segments of code and other POI keyword data exist in the analysis data.
And 3, step 3: associate the four-segment code and the POI to the four-level street. The data is associated under the corresponding standard level four street.
And 4, step 4: and (5) keyword screening and voting. 1) Calculating the ratio of the number of times of each POI appearing in the fourth-level street of the POI to the number of times of the POI appearing in the city of the fourth-level street of the POI to obtain a score; 2) And filtering out data with a score smaller than a certain numerical value (the numerical value acquisition mode is described in detail below) from the associated data.
And 5: and constructing an address map. 1) Splicing and de-duplicating all four-segment code POIs under each four-level street in a space mode; 2) And adding the spliced data into the Elasticissearch index by four fields of province, city, region and POI. 3) Setting the word segmentation mode of the POI field as word segmentation with a blank space; 4) And adding all four-segment code POI into an Elasticissearch custom word library. An example of the Elasticsearch index is shown in fig. 3.
More specifically, the internal implementation of the keyword screening and voting and the address map construction in the steps 4 and 5 is as follows:
1) And calculating the score of the POI on the four-level street, wherein the calculation formula is as follows:
POI score on its level four street = number of times POI appears in its level four street/number of times POI appears in city of its level four street 100
2) The test accuracy of the constructed atlas keeping score larger than a certain value is respectively tested by the experiment, and the score with the highest overall accuracy is used as the score threshold value of the constructed atlas keyword voting:
a: and respectively constructing address maps for the data with scores of more than 10, 20, 30, 40 and 50.
B: and respectively searching the constructed new signing data in each scored address map to obtain the statistical accuracy rate after four levels of streets are obtained, wherein the best accuracy rate score threshold value is more than 20 in practice, an experimental graph for testing different cities is shown in figure 4, and the matching rate and the actual accuracy rate after matching are greatly improved.
3) Field information of the Elasticsearch index:
1. the field types of province, city, district and level four streets are set as [ keyword ].
Poi field type is set to [ text ].
The analyzer of the POI field is set to [ whitespace ], meaning that the POI field is participled in a space way by using an Elasticissearch and then inserted into the inverted index.
And 4, setting the search _ analyzer of the POI field to be [ IK _ max _ word ], wherein the meaning is that an IK participler plug-in is utilized to perform participlation on an input search address, the words of the address can repeatedly appear, and the words can be split as long as the words appear in a word stock.
The invention realizes the construction of an address map system by adopting the Elasticissearch and the four-segment code, and constructs a map by performing inverted index on POI fields through the Elasticissearch. In addition, if more memories are given to the filesystems cache, the memories are basically used during searching, and the performance is very high. Based on four sections of rich code data in the existing express database, the constructed address map is more perfect and accurate. Therefore, the technical scheme of the application has the technical effect of high-performance and high-accuracy address map service. In addition, due to the fact that a keyword screening voting strategy is used, the score with the highest accuracy is obtained through experiments and is screened during construction, and the problem that the searching accuracy is poor due to the fact that original data contain wrong data or data with low relevancy is solved.
Claims (10)
1. An Address knowledge graph construction system based on an Elasticissearch index and four-segment codes is characterized by comprising an Airflow task scheduling module, a data analysis module, a data association module, a keyword screening and voting module, an address graph construction module and a database, wherein,
the Airflow task scheduling module is started to realize the management and scheduling of the address map construction task when the map construction or the online data change needs to be reconstructed;
the database obtains administrative division data from an official statistical department and obtains standard four-level street data;
the data analysis module obtains signing data of the express delivery, the signing data comprises or writes back address data of a standard four-level street, and four-segment codes and POI keyword data in the signing data are analyzed;
the data association module is used for associating the analyzed four-segment codes and the POI keyword data to the corresponding standard four-level street data;
the keyword screening and voting module is used for calculating the ratio of the occurrence frequency of each POI in a four-level street where the POI is located to the occurrence frequency of the POI in a city where the POI is located to obtain a calculated score, setting a preset value, and screening and filtering out data with the score smaller than the preset value in the associated data;
the address map building module is used for splicing and de-duplicating all four-segment code POI under each four-level street in a space mode, adding associated data into an Elasticissearch index in four fields of province, city, region and POI, setting the word segmentation mode of the POI field to be recognized in a space word segmentation mode, and adding all four-segment code POI into an Elasticissearch custom word bank to screen the voted address data to build an address knowledge map.
2. The system for constructing an address knowledge graph based on an Elasticsearch index and four-segment codes as claimed in claim 1, wherein said Airflow task scheduling module invokes the data parsing module when starting.
3. The system for constructing the address knowledge graph based on the Elasticsearch index and the four-segment code as claimed in claim 1, wherein the writing back of the address data of the standard four-level street in the sign-off data means that the sign-off data includes a sign-off point, and the writing back of the data of the standard four-level street is performed according to the address back-push of the sign-off point.
4. The system for constructing address knowledge graph based on Elasticsearch index and four-segment code according to claim 1, wherein the POI data is "point of interest" data, and in the geographic information system, one POI data may be a house, a shop, a mailbox, or a bus station.
5. The system for constructing the address knowledge graph based on the Elasticsearch index and the four-segment code according to claim 1, wherein the score of the POI in the keyword screening and voting module on the four-level street is calculated as follows:
the score of a POI on its level four street = the number of times the POI appears in its level four street/the number of times the POI appears in the city of its level four street 100.
6. The address knowledge graph construction system based on the Elasticsearch index and the four-segment code as claimed in claim 1, wherein the keyword screening voting module is configured to use a score with the highest overall accuracy as a score threshold value for constructing the graph keyword voting for the graph test accuracy rate of the constructed graph with a score larger than a certain value.
7. An elastic search index and four-segment code based address knowledge graph construction method is characterized by comprising the following steps:
step 1, acquiring standard four-level street data, downloading administrative division data from a national statistical department, and importing the downloaded data into a special database;
step 2, analyzing the four-segment code and the POI data to obtain address data in the sign-in data, wherein the address data comprises or writes back address data of a standard four-level street, and the data of the four-segment code and other POI keywords in the address data is analyzed;
step 3, associating the four-segment codes and the POI to standard four-level street data, and associating the analyzed address data to the corresponding standard four-level street;
step 4, keyword screening and voting, namely calculating the ratio of the number of times each POI appears in the four-level street of the POI to the number of times each POI appears in the city where the four-level street of the POI is located, obtaining a score, filtering out the score in the associated data which is less than a certain numerical value, and obtaining address data after screening and voting;
step 5, constructing an address map, constructing the address map by using the screened and voted address data, splicing and de-duplicating all four-segment code POIs under each four-level street in a space mode, adding the spliced data into an Elasticissearch index in four fields of province, city, district and POI, setting the word segmentation mode of the POI field as word segmentation in a space mode, adding all the four-segment codes after word segmentation and the POI data into an Elasticissearch custom word bank, constructing the address map by using the constructed new sign-in data, and searching by using an Elasticissearch engine to obtain the accurate four-level street.
8. The method for constructing an address knowledge graph based on the Elasticissearch index and the four-segment code as claimed in claim 7, wherein the administrative division data downloaded in step 1 is standard four-level street data, and the four-level street data is stored in the database.
9. The method for constructing the address knowledge graph based on the Elasticsearch index and the four-segment code according to claim 7, wherein scores smaller than a certain value in the associated data are filtered out in step 4, and people with scores smaller than the certain value are not associated to obtain address data after the voting is screened.
10. The method for constructing the address knowledge graph based on the Elasticsearch index and the four-segment code according to claim 7, wherein in step 5, an Elasticsearch engine is used to search for the index in the constructed address knowledge graph.
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