Data construction method and device, computer storage medium and electronic equipment

Abstract

The invention discloses a data construction method and device, a computer storage medium and electronic equipment, and the method comprises the steps: carrying out the matching of an obtained first name of a target object with address information in an address library, and determining the position coordinate information of the target object; determining a second name set corresponding to the target object in structured unit information of peripheral address information according to analysis of the peripheral address information selected for the position coordinate information; constructing a second name knowledge base of the target object according to an extended name of the second name determined by extension of the second name in the second name set; the intelligent level of a target object search function can be improved, it is ensured that diversified query requirements of a user can be quickly understood and responded, the search process of the target object is accelerated, user experience is improved, and it is ensured that the position of the target object is quickly and accurately positioned.

Description

Technical Field

[0001] The present application relates to the field of data mining technology, and more particularly to a data construction method and device. The present application also relates to a building alias construction method and device, a computer storage medium and an electronic device. Background Art

[0002] With the continuous development of Internet and computer technology, more and more Internet and computer-based service demands are widely used, such as navigation, delivery and other services. Therefore, positioning is particularly important, because the accuracy of positioning determines the accuracy of the service.

[0003] However, any positioning needs to be based on a specific address or location, and in real life scenarios, buildings usually have official names, that is, standard names, and there are also multiple other names that are different from the official names, that is, aliases. The existence of aliases may affect data services or data processing. Summary of the invention

[0004] The present application provides a data construction method to solve the limitations and accuracy problems caused by the data processing or data service process based on the official name in the prior art.

[0005] The present application provides a data construction method, comprising: Matching the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object; Determine, based on analysis of the surrounding address information selected from the location coordinate information, a second name set corresponding to the target object in the structured unit information of the surrounding address information; A second name knowledge base of the target object is constructed according to an extended name of the second name determined by extending the second name in the second name set.

[0006] In some embodiments, matching the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object includes: Acquire a first name of the target object according to the established information database of the target object; The first name is fuzzily matched with the address information in the established address database to determine the location coordinate information related to the first name.

[0007] In some embodiments, the fuzzy matching of the first name with address information in an established address database to determine location coordinate information related to the target object includes: Performing fuzzy matching on the first name and address information in an established address database to determine candidate address information related to the target object; According to the longitude and latitude data corresponding to the candidate address information, selecting the median of the longitude and latitude data, or selecting the mode of the longitude and latitude data; The longitude and latitude data corresponding to the median or the mode is determined as the position coordinate information corresponding to the target object.

[0008] In some embodiments, the determining, based on the analysis of the surrounding address information selected from the location coordinate information, a second name set corresponding to the target object in the structured unit information of the surrounding address information includes: Selecting the surrounding address information of the target object according to the location coordinate information; Parsing the peripheral address information to determine structural unit information of the peripheral address information; A second name set corresponding to the target object is determined according to the structural unit information.

[0009] In some embodiments, the selecting the surrounding address information of the target object according to the position coordinate information includes: Taking the position coordinate information as the center, grid coding is performed on the search area within a preset range to determine a grid coding area; The surrounding address information is searched according to the grid coding area to obtain the surrounding address information of the target object.

[0010] In some embodiments, the parsing of the peripheral address information to determine the structured unit information of the peripheral address information includes: The peripheral address information is structured according to an address resolution tool to obtain structural unit information constituting the peripheral address information.

[0011] In some embodiments, constructing the second name knowledge base of the target object according to the extended name of the second name determined by name extension of the second name in the second name set includes: updating the second name set according to filtering the second names in the second name set; Performing name extension on the updated second name in the second name set to obtain an extended name of the second name; The second name knowledge base is constructed according to the extended name.

[0012] In some embodiments, updating the second name set according to filtering the second name in the second name set includes: According to the statistical occurrence frequency of the second name in the second name set, filtering the names whose occurrence frequency does not meet the frequency filtering requirement, and / or, according to the similarity between the second name in the second name set and the first name, filtering the names that do not meet the similarity requirement; The second name in the second name set is updated.

[0013] In some embodiments, performing name extension on the updated second name in the second name set to obtain an extended name of the second name includes: The name is extended according to the updated second name in the second name set by using a label propagation method to obtain the extended name.

[0014] In some embodiments, constructing a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set includes: Cleaning up the extension name; A second name knowledge base of the target object is constructed according to the cleaned extended name.

[0015] The present application also provides a data construction device, comprising: A first determining unit, configured to match the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object; A second determining unit, configured to determine a second name set corresponding to the target object in the structured unit information of the surrounding address information according to the analysis of the surrounding address information selected from the location coordinate information; A construction unit is used to construct a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set.

[0016] The present application also provides a method for constructing a building alias, comprising: Matching the acquired official name of the building with the address information in the address database to determine the location coordinate information of the building; Determine, based on analysis of the surrounding address information selected from the location coordinate information, a set of aliases corresponding to the building in the structured unit information of the surrounding address information; A second name knowledge base of the building is constructed according to the extended name of the alias determined by the alias extension in the alias set.

[0017] The present application also provides a computer storage medium, including a computer program. When the computer program is run on an electronic device, the electronic device executes the data construction method as described above, or executes the building alias construction method as described above.

[0018] The present application also provides an electronic device, comprising: processor; The memory is used to store a program for processing data generated by an electronic device. When the program is read and executed by the processor, it executes the data construction method as mentioned above, or executes the building alias construction method as mentioned above.

[0019] Compared with the prior art, this application has the following advantages: The data construction method provided by the present application can obtain the location coordinate information of the target object through fuzzy matching based on the first name of the target object and the address information in the address library, and further improve the precision and accuracy of the location coordinate information through the median or mode of the longitude and latitude data. The second name of the target object is determined by using the structured information of the surrounding address information obtained, so that the target object can have the expression of the second name in addition to the first name. In this process, the validity and accuracy of the second name can be further improved through the corresponding filtering method. In order to ensure the comprehensiveness of the second name, the second name after filtering can also be expanded, and the expanded name can also be used as the second name to establish the target object second name knowledge base, so that the intelligence level of the target object search function can be improved in various subsequent scenario requirements, ensuring that the user's diverse query needs can be quickly understood and responded to, accelerating the search process of the target object, improving the user experience, and ensuring that the target object is quickly and accurately located.

[0020] In a building alias construction method provided by the present application, building aliases are mined using delivery address information, and an alias knowledge base is constructed accordingly. Longitude and latitude information is obtained through fuzzy matching of the building's official name and address, and alias information that may exist in the surrounding area to express the building is mined through the longitude and latitude information. Then, the alias knowledge base can be established by widely collecting nearby delivery address information. In application scenarios such as real estate data processing, a more comprehensive and practical alias knowledge base is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a data construction method provided by this application.

[0022] Figure 2 It is a structural schematic diagram of a data construction device provided by this application.

[0023] Figure 3It is a flow chart of a method for constructing a building alias provided in this application.

[0024] Figure 4 It is a structural schematic diagram of a building alias construction device provided by the present application.

[0025] Figure 5 It is a structural schematic diagram of an electronic device provided by this application. DETAILED DESCRIPTION

[0026] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0027] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The descriptions used in this application and the appended claims, such as "a", "first", and "second", are not limitations on quantity or sequence, but are used to distinguish the same type of information from each other.

[0028] Based on the above background technology, it can be known that aliases other than the official name of an object are of great significance for data processing and data services. In the prior art, traditional alias recognition methods rely on keyword extraction and comparison technology, which extracts key elements in information descriptions, such as street names, significant landmarks, and other related information, and matches these keywords with existing community databases. This traditional method is intuitive and simple, but it also has several limitations. For example, it cannot handle or does not handle abbreviations, spelling errors, or expression diversity, which leads to omissions or mismatches in the recognition results.

[0029] Based on the above, this application provides a data construction method, such as Figure 1 As shown, Figure 1 It is a flow chart of a data construction method provided by the present application, the method comprising: Step S101: matching the acquired first name of the target object with the address information in the address database to determine the location coordinate information of the target object; Step S102: determining a second name set corresponding to the target object in the structured unit information of the surrounding address information according to the analysis of the surrounding address information selected from the location coordinate information; Step S103: constructing a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set.

[0030] The above steps S101 to S103 are described in detail below in conjunction with a specific implementation method. Before the detailed description, some technical terms involved in the embodiments of the present application are explained.

[0031] Formal name: refers to a common name, official name or standard name. In the real estate field, each property carries its unique identity and value. The formal name, as the standard name of the property in legal documents, official records and marketing, ensures the uniqueness and accuracy of the property, and is the basis for ownership, transaction records and administrative management. For example, the formal name of a community, the formal name of an office building, etc.

[0032] Aliases: Additional names given by the market, the public or a specific community in addition to the official name. These aliases may be derived from a significant feature of the project, historical background, surrounding environment or connection with a well-known landmark, or sometimes even from a widely circulated story or event. Aliases are often more vivid, easy to remember and emotional. They can quickly establish cognition and association among the target audience and become an important symbol of the uniqueness of the building.

[0033] LCS (Longest Common Subsequence): An algorithm used to measure string similarity, which evaluates the similarity between two sequences by finding the longest identical subsequence in the two sequences.

[0034] LPA (Label Propagation Algorithm): A semi-supervised learning method that aims to achieve classification by propagating the labels of labeled neighbors to unlabeled data points. It is suitable for community detection and data clustering.

[0035] Regarding step S101: matching the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object.

[0036] In step S101, the target object may be a target building, such as a residential area, an apartment, an office building, a shopping mall, etc., and may also include some historical buildings, tourist attraction buildings, etc. The first name is the official name of the target object. The address database may be a pre-established database that stores address information corresponding to the building. In this embodiment, the address database may be a delivery address database.

[0037] The purpose of step S101 is to determine the location coordinate information of the target object. The specific implementation process may include: Step S101-1: acquiring a first name of the target object according to an established information database of the target object; Step S101 - 2 : fuzzy matching is performed on the first name and the address information in the established address database to determine the coordinate information related to the target object.

[0038] The information database in step S101-1 may be a pre-established database for storing target object related information, such as the official name, establishment time, structural features, etc. of the target building. A first name corresponding to the target object may be obtained through the information database, and in this embodiment, the first name may also be understood as the official name, or may also be understood as identification information of the target object.

[0039] The step S101-2 may be performed by fuzzy matching the first name with a pre-established delivery address library, that is, fuzzy matching the first name with the address information in the delivery address library. In this embodiment, the fuzzy matching may adopt a commonly used matching algorithm, such as: Levenshtein distance (edit distance), Jaccard similarity (Jaccard similarity), Cosine similarity (cosine similarity), TF-IDF algorithm and n-gram model, etc. Among them, Levenshtein distance is an algorithm based on editing operations, including insertion, deletion and replacement operations. The smaller the edit distance, the more similar the two strings are. Jaccard similarity is used to compare the similarity of two sets, and the similarity is measured by calculating the ratio of the intersection to the union. For text data, the text can be divided into words or characters as elements of the set. Cosine similarity is a measure of the angle between two vectors. The text is regarded as a vector, each dimension represents a word or feature, and the similarity is measured by calculating the angle between the vectors. The TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is a text feature extraction method that measures the importance of words by calculating word frequency and inverse document frequency. It can be used to calculate the similarity between texts or perform fuzzy matching. The n-gram model calculates the similarity between texts based on the frequency of continuous subsequences. Common n values ​​include unigram (unary word segmentation), that is, n=1, bigram (binary word segmentation), that is, n=2, and trigram (ternary word segmentation), that is, n=3. The location coordinate information related to the target object can be obtained through fuzzy matching. For the delivery address library, because it stores a large amount of delivery address information, the address information related to the target object will be stored in the delivery address library, and then multiple location coordinate information related to the target object can be obtained through fuzzy matching. In this embodiment, filtering and judgment can be performed based on whether the first name is contained in the delivery address data, that is, data similar to the first name is retained.

[0040] To improve the precision and accuracy of the position coordinate information, in this embodiment, the specific process of step S101-2 may include: Step S101-211: fuzzy matching the first name with address information in the established address database to determine candidate address information related to the target object; Step S101-212: selecting the median of the longitude and latitude data corresponding to the candidate address information, or selecting the mode of the longitude and latitude data; Step S101-213: Determine the longitude and latitude data corresponding to the median or the mode as the position coordinate information corresponding to the target object.

[0041] The mode may be when a certain latitude and longitude has a significantly higher frequency of occurrence than other latitude and longitude data, that is, when it becomes the mode, it is considered that the latitude and longitude can represent a relatively accurate position of the target object.

[0042] In addition to selecting the longitude and latitude data based on the median or mode to determine the location coordinate information corresponding to the first name, it can also be determined by the mean method. Therefore, the specific process of step S101-2 can also be implemented in the following way: Step S101-221: fuzzy matching the first name with address information in the established address database to determine candidate address information related to the target object; Step S101-222: selecting an average value of the longitude and latitude data according to the longitude and latitude data corresponding to the candidate address information; Step S101-223: Determine the longitude and latitude data corresponding to the average value as the position coordinate information corresponding to the target object.

[0043] The above are at least two implementation methods for determining the location coordinate information of the target object. In fact, other implementation methods may also be included, for example, through empirical values, or by inputting the longitude and latitude data corresponding to the candidate address information into the AI ​​intelligent big model to obtain the location coordinate information corresponding to the target object. Therefore, the specific implementation method of step S101-2 is not limited to the two specific examples given above.

[0044] Regarding step S102: according to the analysis of the surrounding address information selected from the position coordinate information, a second name set corresponding to the target object in the structured unit information of the surrounding address information is determined.

[0045] The surrounding address information in step S102 may also be surrounding delivery address information, that is, the surrounding address information is selected based on the location coordinate information of the target object. The structured unit information may be a component unit or component of the address information, such as street, house number, name, POI (point of interest), AOI (area of ​​interest), direction, etc.

[0046] The purpose of step S102 is to determine a second name set corresponding to the target object based on the structured unit information of the surrounding address information, which can also be understood as a second name set corresponding to the first name. The specific implementation process may include: Step S102-1: Selecting the surrounding address information of the target object according to the location coordinate information; Step S102-2: parsing the peripheral address information to determine structured unit information of the peripheral address information; Step S102 - 3 : Determine a second name set corresponding to the target object according to the structural unit information.

[0047] The specific implementation process of step S102-1 may include: Step S102-11: Grid coding is performed on the search area within a preset range with the position coordinate information as the center to determine the grid coding area; in this embodiment, the search can be performed within a preset range of 100 meters in radius with the position coordinates of the target object as the center.

[0048] Step S102 - 12 : searching for surrounding address information according to the grid coding area to obtain surrounding address information of the target object.

[0049] In this embodiment, in order to improve the search efficiency, the determined search area can be grid-coded, so that the preset range is a grid space, which is conducive to improving the search efficiency while also ensuring the search accuracy and precision, and can still maintain efficient search and accurate search accuracy in the face of large-scale data scenarios. Of course, the entire periphery can also be grid-coded, and in order to avoid wasting resources, the grid coding can be performed only within the preset range.

[0050] The specific implementation process of step S102-2 may include: Step S102-21: Structuring the surrounding address information according to the address resolution tool to obtain structural unit information constituting the surrounding address information.

[0051] In this embodiment, the address information can be deeply structured and parsed by methods such as Ptolemy. The parsing process can improve the automation level of data structured processing, and can also improve the flexibility and depth of subsequent analysis and processing. For example: political division information includes information such as province, city, county, and township; road network information includes road names, road numbers, road facilities, etc.; detailed address information includes community names, building numbers, house numbers, etc. Through the address parsing method, the delivery address text is parsed by using the text sequence annotation method. For example, No. 969, Wenyi West Road, Wuchang Street, Yuhang District, Hangzhou City, Zhejiang Province, a certain park can be parsed as province=Zhejiang Province, city=Hangzhou City, district=Yuhang District, town=Wuchang Street, road=Wenyi West Road, road_number=969, poi=a certain park.

[0052] The specific implementation process of step S102-3 may include: Step S102-31: Count the structured unit information and select the unit information whose number of occurrences meets the selection requirement as the second name; specifically, the POI information or other structured unit information may be counted in the structured unit information, or some structured unit information may be mixed and then the unit information whose number of occurrences is counted as the second name; Step S102-31: Establish the second name set according to the second name.

[0053] Regarding step S103: constructing a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set.

[0054] In step S103, the so-called extension can be understood as expanding the second name based on the recognition of the second name, thereby increasing the alias range corresponding to the target object, that is, the range of the second name, that is, the second name can include multiple (the alias can include multiple).

[0055] In order to ensure the accuracy and validity of the second name in the constructed second name knowledge base, the specific implementation process of step S103 may include: Step S103-11: updating the second name set according to filtering processing of the second name in the second name set; in this embodiment, the filtering processing can also be understood as screening processing, that is, filtering out aliases that do not meet the selection requirements from the second name set, and the specific implementation process may include at least two methods: Method 1: Filter the names whose occurrence frequencies in the second name set do not meet the frequency filtering requirements according to the counted occurrence frequencies of the second names in the second name set. For example, by setting a first threshold, when the number of occurrences of a second name is less than the first threshold, it is excluded from the second name set.

[0056] Method 2: Filter the names that do not meet the similarity requirements according to the similarity between the second names in the second name set and the first name. The implementation process of Method 2 can be achieved through the Longest Common Subsequence (LCS), that is, calculate the similarity between the second name and the first name. For example, to calculate the Longest Common Subsequence (LCS) of "Binxing Home" and "Binxing Residential Quarter", the common characters and their order of the two strings can be found first.

[0057] String: A = "Binxing Home", B = "Binxing Residential Quarter", the common part of the two strings is "Binxing Home", and the longest common subsequence is 3. Combining the lengths of the two strings for coverage calculation, the final similarity is 2 * 3 / (4 + 4) = 0.75. Then, by setting a second threshold, it is determined whether the similarity is greater than or equal to the second threshold. If so, it is retained; otherwise, it is excluded. In some other embodiments, methods such as edit distance and cosine similarity can also be used to calculate the similarity between the second name and the first name.

[0058] The two methods can be executed sequentially or one of them can be selected for execution. Of course, in this embodiment, for the sake of easy understanding, the occurrence frequency and similarity are used as examples for illustration, but it is not limited to the above two methods. For example, names that do not meet the requirements can also be excluded through confidence or empirical values, etc.

[0059] Step S103-12: perform name extension on the second name in the updated second name set to obtain the extended name of the second name; the name extension method given in this embodiment is a label propagation method (LPA: Label Propagation Algorithm) or a label propagation algorithm to implement name clustering, and further expand the name similar text set, so as to extract more second names. For example: based on the similarity obtained by the longest common subsequence (LCS) mentioned above, a target object name similarity graph model is constructed, in which the node is the target object (for example: a building), and the edge of the target object is constructed when the similarity exceeds a certain degree. For existing names, a unique label is assigned. For unknown names, their own ID can be used as the initial label. Run LPA through the following steps: Iteratively update the label: for the current node, check the neighbor nodes of the current node, and update its own label according to the majority label of the neighbor nodes. Stop condition: Continue to iterate until the label no longer changes or reaches a certain number of iterations. After multiple iterations, obtain the final label of each second name: based on the final label, cluster the nodes with the same label together to determine the extended name in the second name set similar to the first name.

[0060] Step S103-13: construct the second name knowledge base according to the extended name. To further improve the accuracy and precision of the second name, redundant information may be removed. Therefore, when constructing the second name knowledge base, the following steps may be included: Step S103-131: clean up the extension name; for example, remove abnormal information such as special characters carried in the extension name, where the special characters may be "-", ".", etc.

[0061] Step S103-132: construct a second name knowledge base of the target object according to the cleaned extension name.

[0062] The above is a description of an embodiment of a data construction method provided by the present application. The method can obtain the location coordinate information of the target object through fuzzy matching based on the first name of the target object and the address information in the address library, and further improve the precision and accuracy of the location coordinate information through the median or mode of the longitude and latitude data. The second name of the target object is determined by using the structured information of the surrounding address information obtained, so that the target object can have the expression of the second name in addition to the first name. In this process, the validity and accuracy of the second name can be further improved by a corresponding filtering method. Furthermore, in order to ensure the comprehensiveness of the second name, the filtered second name can also be name expanded, and the expanded name is also used as the second name to establish the target object second name knowledge base, so that the target object search function can be improved in the subsequent various scene requirements. The intelligent level, ensure that the user's diverse query needs can be quickly understood and responded to, accelerate the search process of the target object, improve the user experience, and ensure that the target object is quickly and accurately located.

[0063] The above is a specific description of an embodiment of a data construction method provided by the present application. Corresponding to the embodiment of a data construction method provided above, the present application also discloses an embodiment of a data construction device, please refer to Figure 2 Since the device embodiment is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described below is only illustrative.

[0064] like Figure 2 As shown, Figure 2 is a structural schematic diagram of a data construction device provided by the present application, the device comprising: a first determination unit 201, a second determination unit 202 and a construction unit 203; The first determining unit 201 is used to match the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object; The second determining unit 202 is used to determine a second name set corresponding to the target object in the structured unit information of the surrounding address information according to the analysis of the surrounding address information selected from the location coordinate information; The construction unit 203 is used to construct a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set.

[0065] Among them, the specific implementation process of the first determination unit 201 includes: an acquisition subunit and a determination subunit; the acquisition subunit is used to obtain the first name of the target object based on the established information database of the target object; the determination subunit is used to fuzzy match the first name with the address information in the established address library to determine the location coordinate information related to the first name.

[0066] The specific implementation process of the determination subunit may include: a matching subunit, a selection subunit and a position determination subunit; the matching subunit is used to fuzzy match the first name with the address information in the established address library to determine the candidate address information related to the target object; the selection subunit is used to select the median of the longitude and latitude data, or select the mode of the longitude and latitude data according to the longitude and latitude data corresponding to the candidate address information; the position determination subunit is used to determine the longitude and latitude data corresponding to the median or the mode as the position coordinate information corresponding to the target object.

[0067] For the specific content of the first determining unit 201, please refer to the relevant description of the above step S101, which will not be described in detail here.

[0068] The specific implementation process of the second determination unit 202 may include: a selection subunit, a first determination subunit, and a second determination subunit; the selection subunit is used to select the surrounding address information of the target object according to the location coordinate information; specifically, it may include: a coding subunit, which is used to perform grid coding processing on the search area within a preset range with the location coordinate information as the center, and determine the grid coding area; a search subunit, which is used to search for surrounding address information according to the grid coding area, and obtain the surrounding address information of the target object. The first determination subunit is used to parse the surrounding address information and determine the structural unit information of the surrounding address information; specifically, it is used to perform structural processing on the surrounding address information according to the address resolution tool, and obtain the structural unit information constituting the surrounding address information. The second determination subunit is used to determine the second name set corresponding to the target object according to the structural unit information.

[0069] For the specific content of the second determining unit 202, please refer to the relevant description of the above step S102, which will not be described in detail here.

[0070] The specific implementation process of the third determination unit 203 may include: a filtering subunit, an acquisition subunit and a construction subunit; the filtering subunit is used to update the second name set according to the filtering process of the second name in the second name set; the specific implementation process may include: a first filtering subunit and / or a second filtering subunit, the first filtering subunit is used to filter the names whose occurrence frequency does not meet the frequency filtering requirement according to the statistical occurrence frequency of the second name in the second name set; the second filtering subunit is used to filter the names that do not meet the similarity requirement according to the similarity between the second name in the second name set and the first name; the updating subunit is used to update the second name in the second name set. The acquisition subunit is used to perform name extension on the second name in the updated second name set to obtain the extended name of the second name; specifically, it is used to perform name extension using a label propagation method according to the second name in the updated second name set to obtain the extended name. The construction subunit is used to construct the second name knowledge base according to the extended name; the specific implementation process may include: a cleaning subunit is used to clean the extended name; the construction subunit is specifically used to construct the second name knowledge base of the target object according to the extended name cleaned by the cleaning subunit.

[0071] The above is a description of an embodiment of a data construction device provided in the present application. The specific content of the device embodiment can refer to the content of the method embodiment, and will not be described in detail here.

[0072] Based on the above content, this application also provides a method for constructing a building alias, such as Figure 3 As shown, Figure 3 This is a flowchart of a method for constructing a building alias provided by the present application. The method is mainly a description of a method for constructing a building alias. The building may be a community or a residential area, or an office building, etc., that is, a process of constructing an alias of the residential area according to the formal name of the residential area. The specific implementation process may include: Step S301: matching the acquired official name of the building with the address information in the address database to determine the location coordinate information of the building; Step S302: determining an alias set corresponding to the building in the structured unit information of the surrounding address information according to the analysis of the surrounding address information selected from the location coordinate information; Step S303: constructing a second name knowledge base of the building according to the extended name of the alias determined by extending the alias in the alias set.

[0073] For the specific contents of the above steps S301 to S303, reference may be made to the description of steps S101 to S103 in the above data construction method embodiment, which will not be repeated here.

[0074] Based on the above, the building alias construction method provided by the present application uses the delivery address information to mine the building alias, and builds an alias knowledge base accordingly. The longitude and latitude information is obtained through fuzzy matching of the building's official name and address, and the alias information that may exist in the surrounding area to express the building is mined through the longitude and latitude information. Then, the alias knowledge base can be established by widely collecting the nearby delivery address information. In application scenarios such as real estate data processing, a more comprehensive and practical alias knowledge base is provided.

[0075] Accordingly, the present application also provides a building alias construction device, such as Figure 4 As shown, Figure 4 It is a structural schematic diagram of a building alias construction device provided by the present application. The device embodiment includes: a first determination unit 401, a second determination unit 402 and a construction unit 403.

[0076] The first determining unit 401 is used to match the acquired official name of the building with the address information in the address database to determine the location coordinate information of the building; The second determining unit 402 is used to determine the alias set corresponding to the building in the structured unit information of the surrounding address information according to the analysis of the surrounding address information selected from the location coordinate information; The construction unit 403 is used to construct a second name knowledge base of the building according to the extended name of the alias determined by the alias extension in the alias set.

[0077] Regarding the specific content of the building alias construction device, you can also refer to the content of the above-mentioned data construction method embodiment and the content of the building alias construction method embodiment, which will not be repeated here.

[0078] Based on the above content, the present application also provides a computer storage medium, including a computer program. When the computer program is run on an electronic device, the electronic device executes the relevant content involved in the above-mentioned data construction method, or executes the relevant content involved in the above-mentioned building alias construction method.

[0079] Based on the above content, the present application also provides an electronic device, such as Figure 5 As shown, Figure 5 : is a structural schematic diagram of an electronic device provided by the present application, the electronic device comprising: Processor 501; The memory 502 is used to store a program for processing data generated by an electronic device. When the program is read and executed by the processor, it executes the relevant contents involved in the above-mentioned data construction method, or executes the relevant contents involved in the above-mentioned building alias construction method.

[0080] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0083] 1. Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0084] 2. Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0085] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A data construction method, characterized in that: include: Matching the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object; Determine, based on analysis of the surrounding address information selected from the location coordinate information, a second name set corresponding to the target object in the structured unit information of the surrounding address information; A second name knowledge base of the target object is constructed according to an extended name of the second name determined by extending the second name in the second name set.

2. The data construction method according to claim 1, characterized in that: The step of matching the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object includes: Acquire a first name of the target object according to the established information database of the target object; The first name is fuzzily matched with the address information in the established address database to determine the location coordinate information related to the first name.

3. The data construction method according to claim 2, characterized in that: The fuzzy matching of the first name with the address information in the established address database to determine the location coordinate information related to the target object includes: Performing fuzzy matching on the first name and address information in an established address database to determine candidate address information related to the target object; According to the longitude and latitude data corresponding to the candidate address information, selecting the median of the longitude and latitude data, or selecting the mode of the longitude and latitude data; The longitude and latitude data corresponding to the median or the mode is determined as the position coordinate information corresponding to the target object.

4. The data construction method according to claim 1, characterized in that: The step of determining, based on the analysis of the surrounding address information selected from the location coordinate information, a second name set corresponding to the target object in the structured unit information of the surrounding address information includes: Selecting the surrounding address information of the target object according to the location coordinate information; Parsing the peripheral address information to determine structural unit information of the peripheral address information; A second name set corresponding to the target object is determined according to the structural unit information.

5. The data construction method according to claim 4, characterized in that: The selecting the surrounding address information of the target object according to the position coordinate information includes: Taking the position coordinate information as the center, grid coding is performed on the search area within a preset range to determine a grid coding area; The surrounding address information is searched according to the grid coding area to obtain the surrounding address information of the target object.

6. The data construction method according to claim 4, characterized in that: The step of parsing the surrounding address information to determine the structured unit information of the surrounding address information includes: The peripheral address information is structured according to an address resolution tool to obtain structural unit information constituting the peripheral address information.

7. The data construction method according to claim 1, characterized in that: The step of constructing a second name knowledge base of the target object according to the extended name of the second name determined by name extension of the second name in the second name set includes: updating the second name set according to filtering the second names in the second name set; Performing name extension on the updated second name in the second name set to obtain an extended name of the second name; The second name knowledge base is constructed according to the extended name.

8. The data construction method according to claim 7, characterized in that: The updating of the second name set according to the filtering process of the second name in the second name set includes: According to the statistical occurrence frequency of the second name in the second name set, filtering the names whose occurrence frequency does not meet the frequency filtering requirement, and / or, according to the similarity between the second name in the second name set and the first name, filtering the names that do not meet the similarity requirement; The second name in the second name set is updated.

9. The data construction method according to claim 7 or 8, characterized in that: The step of performing name extension on the updated second name in the second name set to obtain an extended name of the second name includes: The name is extended according to the updated second name in the second name set by using a label propagation method to obtain the extended name.

10. The data construction method according to claim 1, characterized in that: The step of constructing a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set includes: Cleaning up the extension name; A second name knowledge base of the target object is constructed according to the cleaned extended name.

11. A data construction device, characterized in that: include: A first determining unit, configured to match the acquired first name of the target object with the address information in the address library to determine the location coordinate information of the target object; A second determining unit, configured to determine a second name set corresponding to the target object in the structured unit information of the surrounding address information according to the analysis of the surrounding address information selected from the location coordinate information; A construction unit is used to construct a second name knowledge base of the target object according to the extended name of the second name determined by the second name extension in the second name set.

12. A method for constructing a building alias, characterized in that: include: Matching the acquired official name of the building with the address information in the address database to determine the location coordinate information of the building; Determine, based on analysis of the surrounding address information selected from the location coordinate information, a set of aliases corresponding to the building in the structured unit information of the surrounding address information; A second name knowledge base of the building is constructed according to the extended name of the alias determined by the alias extension in the alias set.

13. A computer storage medium, characterized in that: It comprises a computer program, which, when executed on an electronic device, enables the electronic device to execute the data construction method as described in any one of claims 1 to 10, or the building alias construction method as described in claim 12.

14. An electronic device, characterized in that: include: processor; A memory for storing a program for processing data generated by an electronic device, wherein when the program is read and executed by the processor, the program executes the data construction method as described in any one of claims 1 to 10, or executes the building alias construction method as described in claim 12.

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