Related Articles Search Method, Device, Electronic Device and Storage Medium

Abstract

The present disclosure relates to a method, device, electronic device and storage medium for searching related articles. The method determines the search information by determining a first article based on the attribute information included therein. A first preset number of candidate articles are searched based on the search information of the first article, so as to match the first name information and the first address information included in the attribute information of the first article with the second name information and the second address information in each candidate article. Finally, the second article is determined as the article that matches the first article based on the matching value of each candidate article and the first article. The embodiment of the present disclosure can search for related candidate articles through the attribute information in the first article, and establish an association with the candidate article based on the name and address therein, so as to obtain a second article with high relevance, thereby improving the accuracy of the search results of related articles.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a method, device, electronic device and storage medium for searching related articles. Background Art

[0002] When browsing an article, users usually need to browse related articles of the article. Currently, the method of searching for related articles based on an article is to search directly based on the author name. This search method is difficult to search for related articles when the author name is expressed in different ways, resulting in low search result accuracy. Summary of the invention

[0003] In view of this, the present disclosure proposes a related article search method, device, electronic device and storage medium, aiming to improve the accuracy of related article search results.

[0004] According to a first aspect of the present disclosure, a method for searching related articles is provided, the method comprising:

[0005] Determining a first article including attribute information, wherein the attribute information includes at least first person name information and first address information;

[0006] Determining search information corresponding to the first article according to the attribute information;

[0007] Searching a first preset number of candidate articles in the article collection according to the search information, each of the candidate articles including corresponding second name information and second address information;

[0008] Determining a matching value between the first article and each of the candidate articles according to the first person name information and the first address information and each of the second person name information and the second address information;

[0009] A second article is determined from among the candidate articles according to the corresponding matching values.

[0010] In a possible implementation, determining the matching value between the first article and each candidate article according to the first person name information and the first address information and each second person name information and the second address information includes:

[0011] Determining a first similarity between each of the candidate articles and the first article according to the first name information and each of the second name information;

[0012] Determining a second similarity between each of the candidate articles and the first article according to the first address information and each of the second address information;

[0013] A matching value is determined according to the first similarity and the second similarity corresponding to each of the candidate articles.

[0014] In a possible implementation, determining the first similarity between each candidate article and the first article according to the first name information and each second name information includes:

[0015] determining at least one first name included in the first name information and at least one second name included in each of the second name information;

[0016] For each of the first personal names, determining at least one corresponding synonym information and a relevant score corresponding to each of the synonym information;

[0017] For each piece of second person name information, determining the relationship between each piece of second person name and each piece of synonym information included therein;

[0018] The first similarity is determined based on the correlation scores of the synonym information corresponding to each of the second personal names.

[0019] In a possible implementation, for each of the first personal names, determining at least one corresponding synonym information and a correlation score corresponding to each of the synonym information includes:

[0020] Determine at least one synonym information corresponding to each of the first personal names;

[0021] The relevant score is determined by comparing the difference between each of the synonym information and the corresponding first person name.

[0022] In a possible implementation manner, determining at least one synonym information corresponding to each of the first personal names includes:

[0023] In response to the first name being an English name, determining the English surname and the English given name included therein;

[0024] The English surnames and given names corresponding to the first names are respectively input into a plurality of preset name format templates to obtain corresponding synonym information.

[0025] In a possible implementation manner, determining at least one synonym information corresponding to each of the first personal names further includes:

[0026] In response to the first name being a Chinese name, converting the Chinese name into characters to obtain at least one corresponding synonym information;

[0027] Alternatively, determine the Chinese surname and given name included;

[0028] The Chinese surname and Chinese name are converted into English surname and English name by pinyin conversion.

[0029] In a possible implementation, determining the relevant score by comparing the difference between each of the synonym information and the corresponding first person name includes:

[0030] Determine a first score corresponding to each of the first names;

[0031] Determine second scores corresponding to a plurality of preset differences respectively;

[0032] In response to at least one difference between the synonym information and the corresponding first person name, a difference between a first score corresponding to the first person name and a sum of second scores corresponding to the differences is determined as a relevant score.

[0033] In a possible implementation, determining the second similarity between each candidate article and the first article according to the first address information and each piece of the second address information includes:

[0034] The edit distance between the first address information and each of the second address information is calculated to obtain a second similarity between each of the candidate articles and the first article.

[0035] In a possible implementation, determining the second article from each of the candidate articles according to the corresponding matching value includes:

[0036] A second preset number of candidate articles with the largest corresponding matching values ​​are determined as second articles.

[0037] In a possible implementation, determining the second article from each of the candidate articles according to the corresponding matching value includes:

[0038] The candidate article whose corresponding matching value is greater than the matching threshold is determined to be the second article.

[0039] In a possible implementation manner, the attribute information further includes article attributes, and determining the search information corresponding to the first article according to the attribute information includes:

[0040] Extracting feature information from the article attributes, the feature information including at least one keyword corresponding to the first article;

[0041] The search information is determined according to the feature information, the first person name information and the first address information.

[0042] In a possible implementation, the first article is a patent document, and the second article is an academic paper.

[0043] In a possible implementation, the article attributes include at least one of the abstract, claims, specification, patent name, technical field, background technology, invention content and specific implementation method, the first personal name information includes at least one inventor name, and the first address includes the applicant's address.

[0044] In a possible implementation, the second personal name information includes at least one name of a paper author, and the second address includes the address of an institution to which the paper author belongs.

[0045] According to a second aspect of the present disclosure, a device for searching related articles is provided, the device comprising:

[0046] A first article determination module, used to determine a first article including attribute information, wherein the attribute information at least includes first person name information and first address information;

[0047] A search information determination module, configured to determine the search information corresponding to the first article according to the attribute information;

[0048] An article search module, used for searching a first preset number of candidate articles in the article collection according to the search information, each of the candidate articles including corresponding second name information and second address information;

[0049] A data matching module, used for determining a matching value between the first article and each of the candidate articles according to the first person name information and the first address information and each of the second person name information and the second address information;

[0050] The second article determination module is used to determine the second article from each of the candidate articles according to the corresponding matching value.

[0051] In a possible implementation, the data matching module includes:

[0052] A first similarity determination submodule, configured to determine a first similarity between each of the candidate articles and the first article based on the first name information and each of the second name information;

[0053] A second similarity determination submodule, configured to determine a second similarity between each of the candidate articles and the first article according to the first address information and each of the second address information;

[0054] The matching value determination submodule is used to determine the matching value according to the first similarity and the second similarity corresponding to each of the candidate articles.

[0055] In a possible implementation, the first similarity determination submodule includes:

[0056] a name determination unit, configured to determine at least one first name included in the first name information and at least one second name included in each of the second name information;

[0057] a synonym determination unit, configured to determine, for each of the first personal names, at least one corresponding synonym information and a correlation score corresponding to each of the synonym information;

[0058] a name associating unit, configured to determine, for each piece of second name information, a relationship between each piece of second name information and each piece of synonym information;

[0059] The first similarity determination unit is used to determine the first similarity according to the correlation scores of the synonym information corresponding to each of the second personal names.

[0060] In a possible implementation, the synonym determination unit includes:

[0061] a synonym determination subunit, configured to determine at least one synonym information corresponding to each of the first personal names;

[0062] The relevance score determination subunit is used to determine the relevance score by comparing the difference between each of the synonym information and the corresponding first person name.

[0063] In a possible implementation, the synonym determination subunit includes:

[0064] An English information determination subunit, configured to determine an English surname and an English given name included therein in response to the first name being an English name;

[0065] The synonym generation subunit is used to input the English surnames and English given names corresponding to each of the first personal names into a plurality of preset name format templates to obtain corresponding synonym information.

[0066] In a possible implementation, the synonym determination subunit further includes:

[0067] A Chinese information determination subunit, configured to, in response to the first name being a Chinese name, convert the Chinese name into characters to obtain at least one corresponding synonym information;

[0068] Alternatively, determine the Chinese surname and given name included;

[0069] The English information conversion subunit is used to convert the Chinese surname and Chinese name into an English surname and English name by means of pinyin conversion.

[0070] In a possible implementation, the relevant score determination subunit includes:

[0071] A first score determination unit, used to determine a first score corresponding to each of the first names;

[0072] A second score determination subunit, used to determine second scores corresponding to a plurality of preset differences respectively;

[0073] The correlation score calculation subunit is used to determine the difference between the first score corresponding to the first name and the sum of the second scores corresponding to the differences as the correlation score in response to at least one difference between the synonym information and the corresponding first name.

[0074] In a possible implementation, the second similarity determination submodule includes:

[0075] The second similarity determination unit is used to calculate the edit distance between the first address information and each of the second address information to obtain the second similarity between each of the candidate articles and the first article.

[0076] In a possible implementation, the second article determination module includes:

[0077] The first screening submodule is used to determine a second preset number of candidate articles with the largest corresponding matching values ​​as the second articles.

[0078] In a possible implementation, the second article determination module includes:

[0079] The second screening submodule is used to determine that the candidate article whose corresponding matching value is greater than the matching threshold is the second article.

[0080] In a possible implementation, the attribute information further includes article attributes, and the search information determination module includes:

[0081] A feature extraction submodule, configured to extract feature information from the article attributes, wherein the feature information includes at least one keyword corresponding to the first article;

[0082] The search information determination submodule is used to determine the search information according to the feature information, the first person name information and the first address information.

[0083] In a possible implementation, the first article is a patent document, and the second article is an academic paper.

[0084] In a possible implementation, the article attributes include at least one of the abstract, claims, specification, patent name, technical field, background technology, invention content and specific implementation method, the first personal name information includes at least one inventor name, and the first address includes the applicant's address.

[0085] In a possible implementation, the second personal name information includes at least one name of a paper author, and the second address includes the address of an institution to which the paper author belongs.

[0086] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0087] processor;

[0088] a memory for storing processor-executable instructions;

[0089] The processor is configured to call the instructions stored in the memory to execute any of the methods described above.

[0090] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, any of the methods described above is implemented.

[0091] The disclosed embodiment can search for related candidate articles through attribute information in a first article, and associate the candidate articles with the names and addresses therein to obtain a highly relevant second article, thereby improving the accuracy of the search results for related articles.

[0092] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0094] Figure 1 A flowchart showing a method for searching related articles according to an embodiment of the present disclosure is shown;

[0095] Figure 2 A schematic diagram showing a process of determining search information according to an embodiment of the present disclosure;

[0096] Figure 3 A schematic diagram showing a process of determining candidate articles according to an embodiment of the present disclosure;

[0097] Figure 4 A flowchart showing the determination of the matching value of each candidate article according to an embodiment of the present disclosure is shown;

[0098] Figure 5 A schematic diagram showing determining synonym information of a first person name according to an embodiment of the present disclosure;

[0099] Figure 6 A schematic diagram showing a device for searching related articles according to an embodiment of the present disclosure;

[0100] Figure 7 A schematic diagram showing an electronic device according to an embodiment of the present disclosure;

[0101] Figure 8 A schematic diagram showing another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0102] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0103] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0104] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0105] Figure 1 A flowchart of a method for searching for related articles according to an embodiment of the present disclosure is shown. In a possible implementation, the method for searching for related articles of an embodiment of the present disclosure can be performed by a terminal device or other processing device, wherein the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. Other processing devices can be service periods, such as a separate server or a server cluster consisting of multiple servers. In some possible implementations, the method for searching for related articles can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0106] In an exemplary application scenario, the related article search method of the embodiment of the present disclosure can be executed on a predetermined patent document. After searching for multiple related papers, associations are established based on the inventor's name and applicant's address in the patent document, and the author's name and address in the paper, so that the papers matching the patent document can be accurately associated with the multiple papers searched.

[0107] like Figure 1As shown, the related article search method of the embodiment of the present disclosure includes the following steps:

[0108] S10. Determine a first article including attribute information.

[0109] In a possible implementation, the first article is an article of the related article to be retrieved, including attribute information. The first article may be different articles in different application scenarios. For example, when searching for papers related to patents, the first article may be a patent document; when searching for patents related to papers, the first article may be an academic paper; when searching for related column articles related to news information, the first article may be news information.

[0110] Optionally, the attribute information is the content in the first article used to characterize the attribute characteristics of the first article, including at least first person name information and first address information. Among them, the first person name information is used to characterize the name of the relevant person of the first article, and the first address information is used to characterize the relevant address of the first article. Further, for different types of first articles, the corresponding relevant persons and relevant addresses are different. For example, when the first article is an academic paper, the relevant person may be the author of the paper, and the relevant address is the address of the institution to which the author of the paper belongs. When the first article is a patent document, the relevant person may be the inventor of the patent document, and the relevant address may be the address of the applicant of the patent document. Since the author of the article, the inventor and the applicant of the patent can be one or more persons, the first person name information can include at least one first person name related to the first article, and the first address information can include at least one address information related to the first article.

[0111] Furthermore, the attribute information may also include other article attributes for characterizing the features of the first article. For example, when the first article is an academic paper, the article attributes may include the name and abstract of the first article. When the first article is a patent document, the article attributes may include at least one of the abstract, claims, and specification of the first article. Optionally, when the article attributes include the specification, it may include partial or complete contents of the specification, and the partial contents of the specification may include the patent name, technical field, background technology, invention content, and specific implementation methods. When the first article is a short story, the article attributes may include the name and preface.

[0112] Step S20: Determine search information corresponding to the first article according to the attribute information.

[0113] In a possible implementation, corresponding search information is determined according to attribute information in the first article, where the search information represents article features corresponding to the first article and is used to search for candidate articles related to the first article.

[0114] Optionally, the method for determining the search information may be to extract characteristic information from the article attributes, the characteristic information including at least one keyword corresponding to the first article. The search information is then determined based on the characteristic information, the first person name information, and the first address information. For example, at least one keyword is extracted from the article attributes as characteristic information. When the first article is a patent document, the characteristic information includes multiple keywords extracted from at least one of the abstract, claims, specification, patent name, technical field, background technology, invention content, and specific implementation method. When the first article is an academic paper, the characteristic information includes multiple keywords extracted from at least one of the abstract, title, and body of the paper. The characteristic information, the first person name information, and the first address information are then used together as search terms to determine the search information including the above search terms.

[0115] Figure 2 FIG. 1 is a schematic diagram showing a process of determining search information according to an embodiment of the present disclosure. Figure 2 As shown, after determining the attribute information 20 including the article attribute 21, the first person name information 22 and the first address information 23, the corresponding feature information 24 is obtained by extracting the keywords in the article attribute 21. The search information 25 is determined based on the first person name information 22, the first address information 23 and the feature information 24.

[0116] Step S30: Search a first preset number of candidate articles in the article collection according to the search information.

[0117] In a possible implementation, the search information corresponding to step S20 is used to search in a preset article set including multiple articles to obtain a first preset number of candidate articles. Optionally, the search method of the embodiment of the present disclosure can be to search the article set through an ES (elasticsearch) search engine according to the corresponding search information to obtain a first preset number of candidate articles. The retrieval process of the ES search engine is to split and store the content in the search information, form a corresponding retrieval index, and then perform matching retrieval.

[0118] Figure 3 FIG. 2 is a schematic diagram showing a process of determining candidate articles according to an embodiment of the present disclosure. Figure 3 As shown, after determining the first article 30, the search information 31 is determined according to the attribute information therein. Further, according to the search information 31, N articles are searched in the preset article set 32 ​​to obtain as candidate articles 33.

[0119] Furthermore, each candidate article retrieved includes corresponding second person name information and second address information. The second person name information and the second address information are respectively used to characterize the name of the relevant person and the relevant address of the candidate article. Furthermore, for different types of candidate articles, the corresponding relevant persons and relevant addresses are also different. For example, when the candidate article is an academic paper, the relevant person may be the author of the paper, and the relevant address may be the address of the institution to which the author of the paper belongs. When the candidate article is a patent document, the relevant person may be the inventor of the patent document, and the relevant address may be the address of the applicant of the patent document. Since the author of the article, the inventor and the applicant of the patent may be one or more persons, the second person name information may include at least one second person name related to the candidate article, and the second address information may include at least one address information related to the candidate article.

[0120] Step S40: Determine a matching value between the first article and each of the candidate articles according to the first person name information and the first address information and each of the second person name information and the second address information.

[0121] In a possible implementation, the matching values ​​of the first article and each candidate article are obtained by establishing associations between the relevant persons and relevant addresses of the first article and the relevant persons and relevant addresses of the candidate articles. That is, the matching values ​​of the corresponding candidate article and the first article can be obtained according to the matching degree between the first person name information and the second person name information, and the matching degree between the first address information and the second address information.

[0122] Figure 4 A flow chart of determining the matching value of each candidate article according to an embodiment of the present disclosure is shown. Figure 4 As shown, in a possible implementation, the process of determining the matching value of each candidate article includes the following steps:

[0123] Step S41: Determine a first similarity between each of the candidate articles and the first article based on the first name information and each of the second name information.

[0124] In a possible implementation, the first name information of the person related to the first article and the second name information of the person related to each candidate article can be matched to obtain the first similarity between each candidate article and the first article. That is, the first similarity is used to characterize the matching degree of the corresponding article related persons, such as the possibility that the two articles are written by the same author, or the possibility that the two articles are used to describe the same person related events or information.

[0125] Optionally, the process of determining the first similarity between each candidate article and the first article may further include: determining at least one first name included in the first name information, and at least one second name included in each second name information. For each first name, determining at least one corresponding synonym information, and the relevant score corresponding to each synonym information. For each second name information, determining the relationship between each second name and each synonym information included therein, and determining the first similarity based on the relevant score of the synonym information corresponding to each second name.

[0126] That is, the synonym information of each first name in the first name information is determined, and when the second name in the second name information is the same as a synonym information, the corresponding relevance score is determined. The relevance score corresponding to each second name in the second name information is calculated and the first similarity is obtained.

[0127] In a possible implementation, the process of determining the synonym information of each first person name in the first person name information includes: determining at least one synonym information corresponding to each first person name, and determining a relevant score by comparing the difference between each synonym information and the corresponding first person name.

[0128] Optionally, since names are expressed differently in articles of different text types, the synonym information corresponding to the first name can be determined according to the text type of the first name. For example, the same English name can be expressed in different formats. In a possible implementation, in response to the first name being an English name, the English surname and English given name included therein can be determined, and the English surnames and English given names corresponding to each first name can be respectively input into a plurality of preset name format templates to obtain corresponding synonym information.

[0129] That is, when the first person name is an English name, the English name is divided into an English first name and an English last name. At the same time, multiple possible expression formats of English names are predetermined, such as "last name·first name"

[0130] , "first name·last name", "first name, last name" and "first name·last name initial", etc., and each expression format is determined as a corresponding name format template. Further, the English name and English last name obtained by dividing the first person name are input into each name format template to obtain corresponding synonym information. Optionally, the synonym information includes information that is completely the same as the corresponding first person name.

[0131] For example, for the first name "SORIN FABIEN", the English surname "SORIN" and the English given name "FABIEN" are obtained by division. They are respectively input into multiple preset name format templates to obtain corresponding synonym information. When the name format templates are respectively "given name surname", "given name·surname", "given name, surname" and "initial of given name·surname", the output synonym information is respectively "FABIEN SORIN", "FABIEN·SORIN", "FABIEN, SORIN" and "FABIEN,S".

[0132] Further, in order to meet the situation where relevant personnel with Chinese names publish other articles in the English name in pinyin format, when the text type of the first name is Chinese, the English name can be obtained by converting it into the corresponding pinyin, and the corresponding synonym information can be further determined. That is to say, in response to the first name being a Chinese name, the Chinese surname and Chinese given name included therein are determined, and the Chinese surname and Chinese given name are converted into an English surname and an English given name by means of pinyin conversion.

[0133] For example, for the first name "张三 (Zhang San)". The Chinese surname "张 (Zhang)" and the Chinese given name "三 (San)" are obtained by division, and they are converted into the corresponding English surname "Zhang" and English given name "San". Further, the English surname "Zhang" and the English given name "San" are respectively input into multiple preset name format templates to obtain corresponding synonym information.

[0134] Optionally, at least one corresponding synonym information can also be obtained by means of text conversion in response to the first name being a Chinese name. For example, the first name is converted into traditional Chinese characters, or from traditional Chinese characters to simplified Chinese characters. For example, when the first name is the simplified Chinese character "张三 (Zhang San)", the synonym information "張三 (Zhang San)" can be obtained by conversion.

[0135] In a possible implementation manner, after determining the synonym information, determining the relevant score according to the difference between each synonym information and the corresponding first name may include: determining the first score corresponding to each first name, determining the second scores corresponding to multiple preset differences respectively, and in response to there being at least one difference between the synonym information and the corresponding first name, determining the difference between the first score corresponding to the first name and the sum of the second scores corresponding to each difference as the relevant score.

[0136] Among them, the first score is the score when the synonym information is completely consistent with the first person's name, the second score is used to represent the score lost when there is a corresponding difference between the first person's name and the synonym information, and the relevant score is calculated based on the first score and the second score, and is used to represent the score of the degree of relevance between the synonym information and the first person's name. Optionally, the difference can be determined according to each name format template, for example, including different uppercase and lowercase letters, surname abbreviations to initials, removing prefixes, removing suffixes, removing., removing, and retaining the name. In other words, when there is at least one difference between the synonym information and the corresponding name, the sum of the second scores corresponding to each difference is determined, and the difference between the first score and the sum of the second scores is calculated to obtain the relevant score.

[0137] For example, the first score can be set to 1, the second score corresponding to the difference of the surname abbreviation being the first letter is 0.3, the second score corresponding to the difference of uppercase and lowercase is 0.2, and the second score corresponding to the difference of adding a · is 0.1. When the first person name is "SORIN FABIEN", synonym information 1 is "Sorin Fabien", synonym information 2 is "S·FABIEN", and synonym information 3 is "Sorin·Fabien", the correlation score of the first person name with synonym information 1 is 0.8, the correlation score with synonym information 2 is 0.6, and the correlation score with synonym information 3 is 0.7.

[0138] Further, after determining the synonym information corresponding to each second name included in the second name information, the relevant score of each synonym is calculated and the first similarity between the corresponding candidate article and the first article is obtained.

[0139] Figure 5 FIG. 2 is a schematic diagram showing how to determine synonym information of a first person name according to an embodiment of the present disclosure. Figure 5 As shown, for each first person name 50 in the first person name information, the corresponding character type 51 is first determined. When the character type 51 corresponding to the first person name 50 is English, the first person name 50 is divided to obtain an English name and an English surname 52, and a plurality of corresponding synonym information 55 is determined based on the English name and the English surname 52 and a plurality of preset name format templates 54. When the character type 51 corresponding to the first person name 50 is Chinese, the first person name 50 is divided to obtain a Chinese name and a Chinese surname 53, and the Chinese name and the Chinese surname 53 are converted into corresponding English names and English surnames 52, and then a plurality of corresponding synonym information 55 is determined based on the English name and the English surname 52 and a plurality of preset name format templates 54.

[0140] In a possible implementation, when the text type of the first person's name 50 is Chinese, another Chinese writing method of the Chinese surname and Chinese name 53 included in the first person's name 50 can be directly determined through text conversion methods such as simplified and traditional Chinese conversion to obtain corresponding synonym information 55.

[0141] Step S42: Determine a second similarity between each of the candidate articles and the first article according to the first address information and each of the second address information.

[0142] In a possible implementation, the second similarity between each candidate article and the first article is used to characterize the degree of relevance of the corresponding related addresses. Optionally, the second similarity can be determined by calculating the edit distance between the first address information and each second address information to obtain the second similarity between each candidate article and the first article. That is, the edit distance between each candidate article and the first article is used as the corresponding second similarity.

[0143] Furthermore, when the first address information includes multiple first addresses and the second address information includes multiple second addresses, the edit distances of the first addresses and the second addresses are calculated respectively, and the weighted sum is calculated to obtain the corresponding second similarity.

[0144] Step S43: determining a matching value according to the first similarity and the second similarity corresponding to each of the candidate articles.

[0145] In a possible implementation, a matching value representing the degree of matching between each candidate article and the first article may be determined according to the first similarity and the second similarity corresponding to each candidate article. Optionally, the matching value may be calculated by calculating a weighted sum of the first similarity and the second similarity.

[0146] Step S50: Determine the second article from each of the candidate articles according to the corresponding matching value.

[0147] In a possible implementation, after determining the matching value corresponding to each candidate article, the second article is determined from each candidate article according to the corresponding matching value. Optionally, the determination method may be to determine a second preset number of candidate articles with the largest corresponding matching value as the second article. Alternatively, determine the candidate article with a corresponding matching value greater than a matching threshold as the second article.

[0148] Optionally, when the application scenario of the embodiment of the present disclosure is to search for academic papers related to patent documents, the second article finally determined is the academic paper.

[0149] The disclosed embodiment can search for related candidate articles through the attribute information in the first article, and establish associations with the candidate articles based on the names and addresses therein to obtain a second article with high relevance, thereby improving the accuracy of the search results for related articles. Among them, by dividing the names and determining the synonym information in different forms, the problem of missing related articles during the search process due to different forms of corresponding names is avoided.

[0150] It should be noted that although Figure 1The above is an example of a related article search method, but those skilled in the art will appreciate that the present disclosure is not limited thereto. In fact, the user can flexibly set the related article search method of the embodiment of the present disclosure according to personal preferences and / or actual application scenarios, as long as the related articles can be searched based on the name and address to improve the accuracy of the search results.

[0151] Figure 6 A schematic diagram of a related article search device according to an embodiment of the present disclosure is shown. Figure 6 As shown, the related article search device includes:

[0152] A first article determination module 60, configured to determine a first article including attribute information, wherein the attribute information at least includes first person name information and first address information;

[0153] A search information determination module 61, configured to determine the search information corresponding to the first article according to the attribute information;

[0154] An article search module 62, configured to search a first preset number of candidate articles in the article collection according to the search information, each of the candidate articles including corresponding second name information and second address information;

[0155] A data matching module 63, configured to determine a matching value between the first article and each of the candidate articles according to the first person name information and the first address information and each of the second person name information and the second address information;

[0156] The second article determining module 64 is used to determine the second article from each of the candidate articles according to the corresponding matching value.

[0157] In a possible implementation, the data matching module includes:

[0158] A first similarity determination submodule, configured to determine a first similarity between each of the candidate articles and the first article based on the first name information and each of the second name information;

[0159] A second similarity determination submodule, configured to determine a second similarity between each of the candidate articles and the first article according to the first address information and each of the second address information;

[0160] The matching value determination submodule is used to determine the matching value according to the first similarity and the second similarity corresponding to each of the candidate articles.

[0161] In a possible implementation, the first similarity determination submodule includes:

[0162] a name determination unit, configured to determine at least one first name included in the first name information and at least one second name included in each of the second name information;

[0163] a synonym determination unit, configured to determine, for each of the first personal names, at least one corresponding synonym information and a correlation score corresponding to each of the synonym information;

[0164] a name associating unit, configured to determine, for each piece of second name information, a relationship between each piece of second name information and each piece of synonym information;

[0165] The first similarity determination unit is used to determine the first similarity according to the correlation scores of the synonym information corresponding to each of the second personal names.

[0166] In a possible implementation, the synonym determination unit includes:

[0167] a synonym determination subunit, configured to determine at least one synonym information corresponding to each of the first personal names;

[0168] The relevance score determination subunit is used to determine the relevance score by comparing the difference between each of the synonym information and the corresponding first person name.

[0169] In a possible implementation, the synonym determination subunit includes:

[0170] An English information determination subunit, configured to determine an English surname and an English given name included therein in response to the first name being an English name;

[0171] The synonym generation subunit is used to input the English surnames and English given names corresponding to each of the first personal names into a plurality of preset name format templates to obtain corresponding synonym information.

[0172] In a possible implementation, the synonym determination subunit further includes:

[0173] A Chinese information determination subunit, configured to, in response to the first name being a Chinese name, convert the Chinese name into characters to obtain at least one corresponding synonym information;

[0174] Alternatively, determine the Chinese surname and given name included;

[0175] The English information conversion subunit is used to convert the Chinese surname and Chinese name into an English surname and English name by means of pinyin conversion.

[0176] In a possible implementation, the relevant score determination subunit includes:

[0177] A first score determination unit, used to determine a first score corresponding to each of the first names;

[0178] A second score determination subunit, used to determine second scores corresponding to a plurality of preset differences respectively;

[0179] The correlation score calculation subunit is used to determine the difference between the first score corresponding to the first name and the sum of the second scores corresponding to the differences as the correlation score in response to at least one difference between the synonym information and the corresponding first name.

[0180] In a possible implementation, the second similarity determination submodule includes:

[0181] The second similarity determination unit is used to calculate the edit distance between the first address information and each of the second address information to obtain the second similarity between each of the candidate articles and the first article.

[0182] In a possible implementation, the second article determination module includes:

[0183] The first screening submodule is used to determine a second preset number of candidate articles with the largest corresponding matching values ​​as the second articles.

[0184] In a possible implementation, the second article determination module includes:

[0185] The second screening submodule is used to determine that the candidate article whose corresponding matching value is greater than the matching threshold is the second article.

[0186] In a possible implementation, the attribute information further includes article attributes, and the search information determination module includes:

[0187] A feature extraction submodule, configured to extract feature information from the article attributes, wherein the feature information includes at least one keyword corresponding to the first article;

[0188] The search information determination submodule is used to determine the search information according to the feature information, the first person name information and the first address information.

[0189] In a possible implementation, the first article is a patent document, and the second article is an academic paper.

[0190] In a possible implementation, the article attributes include at least one of the abstract, claims, specification, patent name, technical field, background technology, invention content and specific implementation method, the first personal name information includes at least one inventor name, and the first address includes the applicant's address.

[0191] In a possible implementation, the second personal name information includes at least one name of a paper author, and the second address includes the address of an institution to which the paper author belongs.

[0192] Figure 7 A schematic diagram of an electronic device 700 according to an embodiment of the present disclosure is shown. For example, the electronic device 700 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0193] Reference Figure 7 , the electronic device 700 may include one or more of the following components: a processing component 702 , a memory 704 , a power component 706 , a multimedia component 708 , an audio component 710 , an input / output (I / O) interface 712 , a sensor component 714 , and a communication component 716 .

[0194] The processing component 702 generally controls the overall operation of the electronic device 700, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 702 may include one or more modules to facilitate the interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate the interaction between the multimedia component 708 and the processing component 702.

[0195] The memory 704 is configured to store various types of data to support operations on the electronic device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phone book data, messages, pictures, videos, etc. The memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0196] The power supply component 706 provides power to the various components of the electronic device 700. The power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 700.

[0197] The multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front camera and / or a rear camera. When the electronic device 700 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0198] The audio component 710 is configured to output and / or input audio signals. For example, the audio component 710 includes a microphone (MIC), and when the electronic device 700 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 704 or sent via the communication component 716. In some embodiments, the audio component 710 also includes a speaker for outputting audio signals.

[0199] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0200] The sensor assembly 714 includes one or more sensors for providing various aspects of status assessment for the electronic device 700. For example, the sensor assembly 714 can detect the open / closed state of the electronic device 700, the relative positioning of components, such as the display and keypad of the electronic device 700, and the sensor assembly 714 can also detect the position change of the electronic device 700 or a component of the electronic device 700, the presence or absence of user contact with the electronic device 700, the orientation or acceleration / deceleration of the electronic device 700, and the temperature change of the electronic device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0201] The communication component 716 is configured to facilitate wired or wireless communication between the electronic device 700 and other devices. The electronic device 700 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0202] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0203] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 704 including computer program instructions, which can be executed by a processor 720 of an electronic device 700 to perform the above method.

[0204] Figure 8 FIG. 8 is a schematic diagram showing another electronic device 800 according to an embodiment of the present disclosure. For example, the electronic device 800 may be provided as a server. Figure 8 , the electronic device 800 includes a processing component 822, which further includes one or more processors, and a memory resource represented by a memory 832 for storing instructions executable by the processing component 822, such as an application. The application stored in the memory 832 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 822 is configured to execute instructions to perform the above method.

[0205] The electronic device 800 may also include a power supply component 826 configured to perform power management of the electronic device 800, a wired or wireless network interface 850 configured to connect the electronic device 800 to a network, and an input / output (I / O) interface 858. The electronic device 800 may operate based on an operating system stored in the memory 832, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0206] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 832 including computer program instructions, which can be executed by the processing component 822 of the electronic device 800 to perform the above method.

[0207] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0208] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0209] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0210] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0211] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0212] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0213] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0214] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0215] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for searching related articles, characterized in that: The method comprises: Determining a first article including attribute information, wherein the attribute information includes at least first person name information and first address information; Determining search information corresponding to the first article according to the attribute information; Searching a first preset number of candidate articles in the article collection according to the search information, each of the candidate articles including corresponding second name information and second address information; determining at least one first name included in the first name information and at least one second name included in each of the second name information; Determine at least one synonym information corresponding to each of the first personal names; Determine a first score corresponding to each of the first names; Determine second scores corresponding to a plurality of preset differences respectively; In response to at least one difference between the synonym information and the corresponding first person name, determining a difference between a first score corresponding to the first person name and a sum of second scores corresponding to the differences as a relevant score; For each piece of second person name information, determining the relationship between each piece of second person name and each piece of synonym information included therein; determining a first similarity based on the correlation scores of the synonym information corresponding to each of the second names; Determining a second similarity between each of the candidate articles and the first article according to the first address information and each of the second address information; Determine a matching value according to the first similarity and the second similarity corresponding to each of the candidate articles; A second article is determined from among the candidate articles according to the corresponding matching values.

2. The method according to claim 1, characterized in that The determining of at least one synonym information corresponding to each of the first personal names comprises: In response to the first name being an English name, determining the English surname and the English given name included therein; The English surnames and given names corresponding to the first names are respectively input into a plurality of preset name format templates to obtain corresponding synonym information.

3. The method according to claim 2, characterized in that The determining of at least one synonym information corresponding to each of the first personal names further includes: In response to the first name being a Chinese name, converting the Chinese name into characters to obtain at least one corresponding synonym information; Alternatively, determine the Chinese surname and given name included; The Chinese surname and Chinese name are converted into English surname and English name by pinyin conversion.

4. The method according to any one of claims 1 to 3, characterized in that: Determining the second similarity between each candidate article and the first article according to the first address information and each second address information includes: The edit distance between the first address information and each of the second address information is calculated to obtain a second similarity between each of the candidate articles and the first article.

5. The method according to claim 1, characterized in that Determining the second article from each of the candidate articles according to the corresponding matching value includes: A second preset number of candidate articles with the largest corresponding matching values ​​are determined as second articles.

6. The method according to claim 1, characterized in that Determining the second article from each of the candidate articles according to the corresponding matching value includes: The candidate article whose corresponding matching value is greater than the matching threshold is determined to be the second article.

7. The method according to claim 6, characterized in that The attribute information further includes article attributes, and the determining of search information corresponding to the first article according to the attribute information includes: Extracting feature information from the article attributes, the feature information including at least one keyword corresponding to the first article; The search information is determined according to the feature information, the first person name information and the first address information.

8. The method according to claim 7, characterized in that The first article is a patent document, and the second article is an academic paper.

9. The method according to claim 7, characterized in that: The article attributes include at least one of the abstract, claims, specification, patent name, technical field, background technology, invention content and specific implementation methods, the first name information includes at least one inventor name, and the first address includes the applicant's address.

10. The method according to claim 9, characterized in that The second personal name information includes at least one name of a paper author, and the second address includes the address of an institution to which the paper author belongs.

11. A device for searching related articles, characterized in that: The device comprises: A first article determination module, used to determine a first article including attribute information, wherein the attribute information at least includes first person name information and first address information; A search information determination module, configured to determine the search information corresponding to the first article according to the attribute information; An article search module, used for searching a first preset number of candidate articles in the article collection according to the search information, each of the candidate articles including corresponding second name information and second address information; A data matching module, used for determining a matching value between the first article and each of the candidate articles according to the first person name information and the first address information and each of the second person name information and the second address information; A second article determination module, used to determine a second article from each of the candidate articles according to the corresponding matching value; The data matching module comprises: A first similarity determination submodule, configured to determine a first similarity between each of the candidate articles and the first article based on the first name information and each of the second name information; A second similarity determination submodule, configured to determine a second similarity between each of the candidate articles and the first article according to the first address information and each of the second address information; A matching value determination submodule, used to determine a matching value according to the first similarity and the second similarity corresponding to each of the candidate articles; The first similarity determination submodule includes: a name determination unit, configured to determine at least one first name included in the first name information and at least one second name included in each of the second name information; a synonym determination unit, configured to determine, for each of the first personal names, at least one corresponding synonym information and a correlation score corresponding to each of the synonym information; a name associating unit, configured to determine, for each piece of second name information, a relationship between each piece of second name information and each piece of synonym information; A first similarity determination unit, configured to determine a first similarity based on a correlation score of the synonym information corresponding to each of the second personal names; The synonym determination unit comprises: a synonym determination subunit, configured to determine at least one synonym information corresponding to each of the first personal names; A relevance score determination subunit, used to determine a relevance score by comparing the difference between each of the synonym information and the corresponding first person name; The relevant score determination subunit comprises: A first score determination unit, used to determine a first score corresponding to each of the first names; A second score determination subunit, used to determine second scores corresponding to a plurality of preset differences respectively; The correlation score calculation subunit is used to determine the difference between the first score corresponding to the first name and the sum of the second scores corresponding to the differences as the correlation score in response to at least one difference between the synonym information and the corresponding first name.

12. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 10.

13. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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