CN113987128A

CN113987128A - Related article searching method and device, electronic equipment and storage medium

Info

Publication number: CN113987128A
Application number: CN202111301509.6A
Authority: CN
Inventors: 王超超; 王为磊; 屠昶旸; 张济徽
Original assignee: Smart Bud Information Technology Suzhou Co ltd
Current assignee: Smart Bud Information Technology Suzhou Co ltd
Priority date: 2021-11-04
Filing date: 2021-11-04
Publication date: 2022-01-28
Also published as: WO2023078414A1

Abstract

The disclosure relates to a related article searching method, device, electronic equipment and storage medium, which determine search information according to attribute information included in a first article by determining the first article. And searching according to the search information of the first article to obtain a first preset number of candidate articles, and matching with second name information and second address information in the candidate articles through first name information and first address information included in the first article attribute information. And finally, determining the second article as the article matched with the first article according to the matching values of the candidate articles and the first article. According to the embodiment of the disclosure, related candidate articles can be searched through the attribute information in the first article, and the second article with high relevance is obtained according to the association between the name and the address of the first article and the candidate articles, so that the accuracy of the related article searching result is improved.

Description

Related article searching method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a related article searching method and apparatus, an electronic device, and a storage medium.

Background

When browsing an article, a user will often have a need to browse articles related to the article. At present, a method for searching related articles based on one article is to search directly based on author names, and the searching method is difficult to search related articles under the condition that author names are represented in different ways, so that the accuracy of search results is low.

Disclosure of Invention

In view of this, the present disclosure provides a related article searching method, an apparatus, an electronic device and a storage medium, which aim to improve the accuracy of related article searching results.

According to a first aspect of the present disclosure, there is provided a related article searching method, the method comprising:

determining a first article comprising attribute information, wherein the attribute information at least comprises first 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 an article set according to the search information, wherein each candidate article comprises corresponding second name information and second address information;

determining a matching value of the first article and each candidate article according to the first name information and the first address information and each second name information and each second address information;

and determining a second article in each candidate article according to the corresponding matching value.

In one possible implementation manner, the determining, according to the first name information and the first address information and the second name information and the second address information, a matching value of the first article and each of the candidate articles includes:

determining a first similarity between each candidate article and a first article according to the first name information and each second name information;

determining a second similarity of each candidate article and the first article according to the first address information and each second address information;

and determining a matching value according to the first similarity and the second similarity corresponding to each candidate article.

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

determining at least one first person included in the first person information and at least one second person included in each second person information;

for each first person name, determining corresponding at least one synonym information and a relevant score corresponding to each synonym information;

for each piece of second person name information, determining the relationship between each second person name included in the second person name information and each piece of synonym information;

and determining a first similarity according to the relevant score sum of the synonym information corresponding to each second person name.

In a possible implementation manner, the determining, for each first person name, corresponding at least one piece of synonym information, and the associated score corresponding to each piece of synonym information includes:

determining at least one synonym information corresponding to each first person;

and determining a relevance score by comparing the difference between each synonym information and the corresponding first person name.

In a possible implementation manner, the determining at least one synonym information corresponding to each first person includes:

in response to the first person being an English person, determining an English last name and an English first name included therein;

and inputting the English surname and the English first name corresponding to each first name into a plurality of preset name format templates respectively to obtain corresponding synonym information.

In a possible implementation manner, the determining at least one synonym information corresponding to each first person further includes:

responding to the first name as a Chinese name, and performing character conversion on the Chinese name to obtain at least one corresponding synonym information;

or determining the Chinese surname and Chinese first name included in the Chinese surname;

and converting the Chinese surname and the Chinese first name into the English surname and the English first name in a pinyin conversion mode.

In one possible implementation, the determining the relevance score by comparing the difference between each of the synonym information and the corresponding first person name includes:

determining a first score corresponding to each first person;

determining second scores corresponding to the preset differences respectively;

and in response to the synonym information having at least one difference with the corresponding first person, determining a difference between a first score corresponding to the first person and a sum of second scores corresponding to each difference as a relevant score.

In one possible implementation manner, the determining, according to the first address information and each piece of the second address information, a second similarity between each piece of the candidate articles and the first article includes:

and calculating the editing distance of the first address information and each second address information to obtain a second similarity of each candidate article and the first article.

In one possible implementation manner, the determining, according to the corresponding matching value, a second article in each of the candidate articles includes:

and determining a second preset number of candidate articles with the maximum corresponding matching value as second articles.

and determining the candidate article with the corresponding matching value larger than the matching threshold value as the second article.

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

extracting feature information from the article attributes, wherein the feature information comprises at least one keyword corresponding to the first article;

and determining search information according to the characteristic information, the first name information and the first address information.

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

In one possible implementation, the article attribute includes at least one of a specification abstract, a claim, a specification, a patent name, a technical field, a background, an inventive content, and a specific implementation, the first person name information includes at least one inventor name, and the first address includes an applicant address.

In a possible implementation, the second name information includes at least one name of a paper author, and the second address includes an address of an organization to which the paper author belongs.

According to a second aspect of the present disclosure, there is provided a related article search apparatus, the apparatus comprising:

the first article determining module is used for determining a first article comprising attribute information, wherein the attribute information at least comprises first name information and first address information;

the search information determining module is used for determining search information corresponding to the first article according to the attribute information;

the article searching module is used for searching a first preset number of candidate articles in an article set according to the searching information, and each candidate article comprises corresponding second name information and second address information;

the data matching module is used for determining the matching values of the first article and each candidate article according to the first name information and the first address information and each second name information and each second address information;

and the second article determining module is used for determining a second article in each candidate article according to the corresponding matching value.

In one possible implementation, the data matching module includes:

a first similarity determining submodule, configured to determine first similarities between the candidate articles and the first article according to the first name information and the second name information;

a second similarity determining submodule, configured to determine a second similarity between each candidate article and the first article according to the first address information and each second address information;

and the matching value determining submodule is used for determining a matching value according to the first similarity and the second similarity corresponding to each candidate article.

In one possible implementation manner, the first similarity determining submodule includes:

a person name determining unit configured to determine at least one first person name included in the first person name information and at least one second person name included in each of the second person name information;

the synonym determining unit is used for determining at least one corresponding synonym information and a relevant score corresponding to each synonym information for each first person name;

a person name association unit configured to determine, for each piece of the second person name information, a relationship between each piece of the second person name included in the piece of the second person name information and each piece of the synonym information;

and the first similarity determining unit is used for determining the first similarity according to the relevant score sum of the synonym information corresponding to each second person name.

In one possible implementation, the synonym determining unit includes:

a synonym determining subunit, configured to determine at least one synonym information corresponding to each first person;

and the related score determining subunit is used for determining related scores by comparing the difference between each synonym information and the corresponding first person.

In one possible implementation, the synonym determination subunit includes:

the English information determining subunit is used for determining English surnames and English first names which are included in the first person name in response to the first person name being an English person name;

and the synonym generating subunit is used for respectively inputting the English surname and the English first name corresponding to each first name into a plurality of preset name format templates to obtain corresponding synonym information.

In one possible implementation, the synonym determination subunit further includes:

the Chinese information determining subunit is used for responding to the first name as the Chinese name and performing character conversion on the Chinese name to obtain at least one corresponding synonym information;

and the English information conversion subunit is used for converting the Chinese surname and the Chinese first name into the English surname and the English first name in a pinyin conversion mode.

In one possible implementation, the relevance score determining subunit includes:

a first score determining unit, configured to determine a first score corresponding to each first person;

the second score determining subunit is used for determining second scores corresponding to the preset differences respectively;

and the related score calculating subunit is used for responding to at least one difference between the synonym information and the corresponding first person, and determining the difference between the first score corresponding to the first person and the sum of the second scores corresponding to the differences as the related score.

In one possible implementation manner, the second similarity determination submodule includes:

and the second similarity determining unit is used for calculating the editing distance of the first address information and each second address information to obtain the second similarity between each candidate article and the first article.

In one possible implementation, the second article determination module includes:

and the first screening submodule is used for determining a second preset number of candidate articles with the maximum corresponding matching value as second articles.

and the second screening submodule is used for determining the candidate article of which the corresponding matching value is greater than the matching threshold value as a second article.

In one possible implementation manner, the attribute information further includes an article attribute, and the search information determining module includes:

the feature extraction submodule is used for extracting feature information from the article attributes, and the feature information comprises at least one keyword corresponding to the first article;

and the search information determining submodule is used for determining search information according to the characteristic information, the first name information and the first address information.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of the above.

According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method as defined in any one of the above.

According to the embodiment of the disclosure, related candidate articles can be searched through the attribute information in the first article, and the second article with high relevance is obtained according to the association between the name and the address of the first article and the candidate articles, so that the accuracy of the related article searching result is improved.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

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

FIG. 1 illustrates a flow chart of a related article search method according to an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of a process of determining search information according to an embodiment of the present disclosure;

FIG. 3 shows a schematic diagram of a process of determining candidate articles according to an embodiment of the present disclosure;

FIG. 4 illustrates a flow chart for determining a matching value for each candidate article according to an embodiment of the disclosure;

FIG. 5 illustrates a schematic diagram of determining first person name synonym information, according to an embodiment of the present disclosure;

FIG. 6 shows a schematic diagram of a related article search apparatus according to an embodiment of the present disclosure;

FIG. 7 shows a schematic diagram of an electronic device in accordance with an embodiment of the disclosure;

fig. 8 shows a schematic diagram of another electronic device according to an embodiment of the present disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

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

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

Fig. 1 shows a flowchart of a related article search method according to an embodiment of the present disclosure. In one possible implementation manner, the related article searching method of the embodiment of the disclosure may be performed by a terminal device or other processing device, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like. The other processing devices may be a service period, such as a single server or a cluster of servers. In some possible implementations, the related article search method can be implemented by a processor calling computer readable instructions stored in a memory.

In an exemplary application scenario, after a plurality of related papers are searched, association is established between the inventor name and the applicant address in the patent document and between the author name and the address in the papers, and the papers matching the patent document are accurately associated in the searched plurality of papers by executing the related article searching method of the embodiment of the disclosure on a predetermined patent document.

As shown in fig. 1, the related article searching method of the embodiment of the present disclosure includes the following steps:

and S10, determining the first article comprising the attribute information.

In one possible implementation, the first article is an article of a related article to be retrieved, which includes attribute information. The first article may be a different article in different application scenarios. For example, in searching for patent-related papers, the first article may be a patent document; in searching for patents related to the article, the first article may be an academic article; when searching for related articles in a section related to news information, the first article may be news information.

Optionally, the attribute information is content in the first article for characterizing attribute characteristics of the first article, and includes at least first name information and first address information. The first person name information is used for representing names of persons related to the first article, and the first address information is used for representing addresses related to the first article. Further, for different types of first articles, the corresponding related persons and related addresses are different. For example, where 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. Where 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, the inventor and the applicant of the article may be one or more persons, that is, the first name information may include at least one first name associated with the first article, and the first address information may include at least one address information associated with the first article.

Further, the attribute information may also include other article attributes for characterizing the first article. For example, where the first article is a scholarly paper, the article attributes may include a name and abstract of the first article. Where the first article is a patent document, the article attributes may include at least one of a specification abstract, a claim, and a specification of the first article. Optionally, when the article attribute includes the specification, a part of the content or the whole content of the specification may be included, and the part of the content of the specification may include a patent name, a technical field, a background, an invention content, and a detailed description. Where the first article is a short novel, the article attributes may include a name and a preamble.

And step S20, determining the search information corresponding to the first article according to the attribute information.

In one possible implementation manner, corresponding search information is determined according to attribute information in the first article, and the search information represents article features corresponding to the first article and is used for searching candidate articles related to the first article.

Optionally, the determining of the search information may be by extracting feature information from an article attribute, where the feature information includes at least one keyword corresponding to the first article. And determining search information according to the characteristic information, the first name information and the first address information. For example, at least one keyword is extracted as the feature information from the article attribute, and when the first article is a patent document, the feature information includes a plurality of keywords extracted from at least one of a specification abstract, a claim, a specification, a patent name, a technical field, a background, an inventive content, and a specific embodiment. When the first article is an academic paper, the feature information includes a plurality of keywords extracted from at least one of a abstract of the paper, a title of the paper and a text of the paper. And then, the characteristic information, the first person information and the first address information are taken as search words together, and the search information comprising the search words is determined.

Fig. 2 shows a schematic diagram of a process of determining search information according to an embodiment of the present disclosure. As shown in fig. 2, after the attribute information 20 including the article attribute 21, the first-person information 22, and the first address information 23 is determined, the corresponding feature information 24 is obtained by extracting keywords in the article attribute 21. The search information 25 is determined from the first person name information 22, the first address information 23 and the feature information 24.

And step S30, searching a first preset number of candidate articles in the article set according to the search information.

In a possible implementation manner, with the search information corresponding to step S20, a preset article set including a plurality of articles is searched for, so as to obtain a first preset number of candidate articles. Optionally, the search method according to the embodiment of the present disclosure may search the article set by using an es (elastic search) search engine according to the corresponding search information, so as 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 carry out matching retrieval.

FIG. 3 shows a schematic diagram of a process of determining candidate articles according to an embodiment of the disclosure. As shown in fig. 3, after the first article 30 is determined, search information 31 is determined based on attribute information therein. Further, N articles are searched in a preset article set 32 according to the search information 31 to obtain candidate articles 33.

Furthermore, each candidate article obtained through retrieval comprises corresponding second name information and second address information. The second name information and the second address information are respectively used for representing the names and the addresses of the relevant persons of the candidate articles. Further, for different types of candidate articles, the corresponding related persons and related addresses are also different. For example, where the candidate article is an academic paper, the associated person may be the author of the paper and the associated address is the address of the institution to which the author of the paper belongs. Where the candidate article is a patent document, the associated person may be the inventor of the patent document and the associated address may be the address of the applicant of the patent document. Since the author, the inventor and the applicant of the article may be one or more persons, that is, the second name information may include at least one second name related to the candidate article, and the second address information may include at least one address information related to the candidate article.

Step S40, determining matching values of the first article and the candidate articles according to the first name information and the first address information and the second name information and the second address information.

In a possible implementation manner, the association is established through the relevant persons and relevant addresses of the first article and the relevant persons and relevant addresses of the candidate articles, so that the matching values of the first article and each candidate article are obtained. That is, the matching value of the corresponding candidate article and the first article can be obtained according to the matching degree of the first name information and the second name information and the matching degree of the first address information and the second address information.

Fig. 4 shows a flow chart for determining matching values of candidate articles according to an embodiment of the disclosure. As shown in fig. 4, in one possible implementation, the process of determining the matching value of each candidate article includes the following steps:

step S41, determining a first similarity between each candidate article and the first article according to the first name information and each second name information.

In a possible implementation manner, the first similarity between each candidate article and the first article may be obtained by matching according to the first person information of the person related to the first article and the second person information of the person related to each candidate article. That is, the first similarity is used to characterize the matching degree of people related to the corresponding article, such as the possibility that two articles are the same author, or the possibility that two articles are used to describe events or information related to the same person.

Optionally, the process of determining the first similarity between each candidate article and the first article may further include: at least one first person name included in the first person name information and at least one second person name included in each second person name information are determined. And for each first person, determining corresponding at least one synonym information and a relevant score corresponding to each synonym information. And for each second person name information, determining the relationship between each second person name and each synonym information, and determining the first similarity according to the relevant score of the synonym information corresponding to each second person name.

That is, the synonym information of each first person in the first person information is determined, and when a second person in the second person information is the same as one synonym information, the corresponding relevance score is determined. And calculating the corresponding relevant scores of the second names in the second name information to obtain the first similarity.

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

Optionally, because the expression ways of the names in the articles with different text types are different, the synonym information corresponding to the first name may be determined according to the text type of the first name, for example, the same english name may be represented in different formats. In a possible implementation manner, in response to that the first name is an english name, determining an english surname and an english first name included therein, and inputting the english surname and the english first name corresponding to each first name into a plurality of preset name format templates respectively to obtain corresponding synonym information.

That is, when the first person is an english person, the english person is divided into english first name and english last name. At the same time, the possible representation formats of a plurality of English names, e.g. "first name"

First name/last name, initial of first name/last name, etc. each expression format is determined as a corresponding name format template. Further, English first names and English last names obtained by dividing the first names are input into each name format template, and corresponding synonym information is obtained. Optionally, the synonym information includes information identical to the corresponding first person name.

For example, for the first person name "SORIN FABIEN". English surnames 'SORIN' and English names 'FABIEN' are obtained through division, and are respectively input into a plurality of preset name format templates to obtain corresponding synonym information. When each name format template is "first name/last name", and "first initial of first name/last name", the synonym information outputted is "FABIEN sort", "FABIEN/sort", "FABIEN, sort", and "FABIEN, S", respectively.

Further, in order to satisfy the situation that the related person with the Chinese name publishes other articles with the English name in the Pinyin format, when the character type of the first person name is Chinese, the first person name can be converted into the corresponding Pinyin to obtain the English name, and the corresponding synonym information can be further determined. That is, in response to the first person being a chinese person, the chinese surname and the chinese first name included therein are determined, and the chinese surname and the chinese first name are converted into an english surname and an english first name by pinyin conversion.

For example, for the first person name "Zhang three". Dividing to obtain Chinese family name 'Zhang' and Chinese name 'III', converting them into correspondent English family name 'Zhang' and English name 'San'. And further inputting the English surname 'Zhang' and the English name 'San' into a plurality of preset name format templates respectively to obtain corresponding synonym information.

Optionally, at least one corresponding synonym information may be obtained by a text conversion mode in response to that the first name is a chinese name. For example, the first person name is converted from a traditional character to a traditional character, or from a traditional character to a simplified character. For example, when the first person is the simplified character "three by three", it can be converted into the traditional character to obtain the synonym information " three".

In one possible implementation manner, after determining the synonym information, determining the relevance score according to a difference between each synonym information and the corresponding first person name may include: and determining a first score corresponding to each first person, determining second scores corresponding to multiple preset differences, and determining a difference between the first score corresponding to the first person and the sum of the second scores corresponding to the differences as a related score in response to at least one difference between the synonym information and the corresponding first person.

The first score is a score when the synonym information is completely consistent with the first person, the second score is used for representing a score lost when the first person is different from the synonym information correspondingly, and the related score is obtained by calculation according to the first score and the second score and is used for representing the synonym information and the first person related degree score. Alternatively, the differences may be determined from each name format template, which may include, for example, case differences, initials, prefix removal, suffix removal, and reserved names. That is, in the case where there is at least one difference between the synonym information and the corresponding name, a second score sum corresponding to each difference is determined, and the difference between the first score sum and the second score sum is calculated to obtain a correlation score.

For example, the first score may be set to 1, the second score corresponding to a difference in the acronym of the last name may be set to 0.3, the second score corresponding to a difference in case may be set to 0.2, and the second score corresponding to a difference in ascending · ascending may be set to 0.1. When the first person name is "SORIN FABIEN", the synonym information 1 is "SORIN FABIEN", the synonym information 2 is "S · FABIEN", and the synonym information 3 is "SORIN · FABIEN", the first person name has a correlation score with the synonym information 1 of 0.8, a correlation score with the synonym information 2 of 0.6, and a correlation score with the synonym information 3 of 0.7.

Further, after determining the synonym information corresponding to each second person included in the second person information, calculating the relevant score of each synonym and obtaining the first similarity between the corresponding candidate article and the first article.

FIG. 5 illustrates a schematic diagram of determining first person name synonym information, according to an embodiment of the disclosure. As shown in fig. 5, for each first person 50 in the first person information, a corresponding character type 51 is first determined. When the character type 51 corresponding to the first name 50 is english, the first name 50 is divided into english names and english surnames 52, and a plurality of corresponding synonym information 55 are determined according to the english names and english surnames 52 and a plurality of preset name format templates 54. When the character type 51 corresponding to the first person 50 is Chinese, the first person 50 is divided to obtain a Chinese name and a Chinese surname 53, the Chinese name and the Chinese surname 53 are converted into a corresponding English name and an English surname 52, and a plurality of corresponding synonym information 55 are determined according to the English name and the English surname 52 and a plurality of preset name format templates 54.

In a possible implementation manner, when the text type of the first personal name 50 is chinese, another chinese writing manner of the chinese last name and the chinese first name 53 included in the first personal name 50 may be determined directly through a text conversion manner such as simplified-traditional conversion, and the corresponding synonym information 55 is obtained.

Step S42, determining a second similarity between each candidate article and the first article according to the first address information and each second address information.

In one possible implementation, the second similarity between each candidate article and the first article is used to characterize the degree of correlation of the corresponding related address. Optionally, the second similarity may be determined by calculating an edit distance between the first address information and each second address information to obtain a second similarity between each candidate article and the first article. That is, the edit distance between each candidate article and the first article is taken as the corresponding second similarity.

Further, when the first address information includes a plurality of first addresses and the second address information includes a plurality of second addresses, the edit distances of the first addresses and the second addresses are respectively calculated, and the weighted sum is calculated to obtain corresponding second similarity.

Step S43, determining a matching value according to the first similarity and the second similarity corresponding to each candidate article.

In a possible implementation manner, a matching value used for characterizing the matching degree of each candidate article with the first article may be determined according to the first similarity and the second similarity corresponding to each candidate article. Alternatively, the matching value may be calculated by calculating a weighted sum of the first similarity and the second similarity.

And step S50, determining a second article in each candidate article according to the corresponding matching value.

In one possible implementation manner, after the matching value corresponding to each candidate article is determined, the second article is determined in each candidate article according to the corresponding matching value. Optionally, the determining means may be that a second preset number of candidate articles with the largest corresponding matching value are determined as second articles. Or determining the candidate article with the corresponding matching value larger than the matching threshold value as the second article.

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

According to the embodiment of the disclosure, related candidate articles can be searched through the attribute information in the first article, and the second article with high relevance is obtained according to the association between the name and the address of the first article and the candidate articles, so that the accuracy of the related article searching result is improved. The names are divided and then determined as synonym information in different expression forms, so that the problem that related articles are missed in the searching process due to different expression forms of the corresponding names is solved.

It should be noted that, although the related article search method is described above by taking fig. 1 as an example, those skilled in the art will understand that the disclosure should not be limited thereto. In fact, the user can flexibly set the related article searching method according to personal preferences and/or actual application scenarios, as long as the related articles can be searched based on names and addresses, and the accuracy of the search result is improved.

Fig. 6 shows a schematic diagram of a related article search apparatus according to an embodiment of the present disclosure. As shown in fig. 6, the related article search device includes:

a first article determining module 60, configured to determine a first article including attribute information, where the attribute information includes at least first name information and first address information;

a search information determining module 61, configured to determine, according to the attribute information, search information corresponding to the first article;

the article searching module 62 is configured to search a first preset number of candidate articles in an article set according to the search information, where each candidate article includes corresponding second name information and second address information;

a data matching module 63, configured to determine matching values of the first article and each candidate article according to the first name information and the first address information and the second name information and the second address information;

and a second article determining module 64, configured to determine a second article in each candidate article according to the corresponding matching value.

In one possible implementation, the data matching module includes:

In one possible implementation, the synonym determining unit includes:

In one possible implementation, the synonym determination subunit includes:

Fig. 7 shows a schematic diagram of an electronic device 700 according to an embodiment of the disclosure. 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, an exercise device, a personal digital assistant, and the like.

Referring to fig. 7, 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.

The processing component 702 generally controls overall operation of the electronic device 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 702 may include one or more processors 720 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 702 may include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.

The memory 704 is configured to store various types of data to support operations at the electronic device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 704 may be implemented by any type or combination of volatile or non-volatile memory devices 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 or optical disks.

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

The multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and a 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 an input signal from a 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 boundary of a 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 facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 700 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

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

The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be 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.

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 may detect an open/closed state of the electronic device 700, the relative positioning of components, such as a display and keypad of the electronic device 700, the sensor assembly 714 may also detect a change in the position 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, orientation or acceleration/deceleration of the electronic device 700, and a change in the temperature of the electronic device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 714 may also include a light 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.

The communication component 716 is configured to facilitate wired or wireless communication between the electronic device 700 and other devices. The electronic device 700 may 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 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 704 including computer program instructions executable by the processor 720 of the electronic device 700 to perform the above-described method, is also provided.

Fig. 8 shows a schematic diagram of another electronic device 800 according to an embodiment of the disclosure. For example, the electronic device 800 may be provided as a server. Referring to fig. 8, electronic device 800 includes a processing component 822, which further includes one or more processors, and memory resources, represented by memory 832, for storing instructions, such as applications, that are executable by processing component 822. The application programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processing component 822 is configured to execute instructions to perform the above-described methods.

The electronic device 800 may also include a power 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 XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 832, is also provided that includes computer program instructions executable by the processing component 822 of the electronic device 800 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or 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 may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method of searching for related articles, the method comprising:

2. The method of claim 1, wherein determining a match value for the first article and each of the candidate articles based on the first person information and first address information and each of the second person information and second address information comprises:

3. The method of claim 2, wherein determining a first similarity of each of the candidate articles to the first article based on the first name information and each of the second name information comprises:

4. The method of claim 3, wherein for each of the first names, determining a corresponding at least one synonym information, and wherein the relevance score for each of the synonym information comprises:

5. The method of claim 4, wherein the determining at least one synonym information for each of the first names comprises:

6. The method of claim 5, wherein the determining at least one synonym information for each of the first names further comprises:

7. The method according to any one of claims 4-6, wherein determining a relevance score by comparing the difference between each of the synonym information and the corresponding first person name comprises:

determining a first score corresponding to each first person;

determining second scores corresponding to the preset differences respectively;

8. The method of any of claims 2-6, wherein determining the second similarity of each of the candidate articles to the first article based on the first address information and each of the second address information comprises:

9. The method of claim 1, wherein determining a second article among each of the candidate articles according to the corresponding match value comprises:

10. The method of claim 1, wherein determining a second article among each of the candidate articles according to the corresponding match value comprises:

11. The method of claim 10, wherein the attribute information further includes an article attribute, and the determining the search information corresponding to the first article according to the attribute information includes:

12. The method of claim 11, wherein the first article is a patent document and the second article is a scholarly paper.

13. The method of claim 11, wherein the article attributes comprise at least one of a specification abstract, a claim, a specification, a patent name, a technical field, a background, an inventive content, and a detailed description, wherein the first person name information comprises at least one inventor name, and wherein the first address comprises an applicant address.

14. The method of claim 13, wherein the second person name information comprises at least one paper author name, and wherein the second address comprises an address of an organization to which the paper author belongs.

15. An apparatus for searching for related articles, the apparatus comprising:

16. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 14.

17. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 14.