WO2019091026A1 - Procédé de recherche rapide de document dans une base de connaissances, serveur d'application, et support d'informations lisible par ordinateur - Google Patents

Procédé de recherche rapide de document dans une base de connaissances, serveur d'application, et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2019091026A1
WO2019091026A1 PCT/CN2018/077675 CN2018077675W WO2019091026A1 WO 2019091026 A1 WO2019091026 A1 WO 2019091026A1 CN 2018077675 W CN2018077675 W CN 2018077675W WO 2019091026 A1 WO2019091026 A1 WO 2019091026A1
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word
search
query
words
keyword
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PCT/CN2018/077675
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English (en)
Chinese (zh)
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张师琲
侯丽
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/237—Lexical tools
    • G06F40/247—Thesauruses; Synonyms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

Definitions

  • the present application relates to the field of data analysis technologies, and in particular, to a quick retrieval method of a knowledge base document, and an application server computer readable storage medium.
  • the present application proposes a quick retrieval method and an application server for a knowledge base document to solve the problem of how to quickly and accurately retrieve the files in the knowledge base and how to quickly understand the main contents of the retrieved files.
  • the present application provides a method for quickly searching a knowledge base document, the method comprising the steps of:
  • the search information is analyzed and processed to obtain a query word
  • the sorted search results are output and correspond to the summary and keywords of the output target document.
  • the present application further provides an application server, including a memory, a processor, and a knowledge base document fast retrieval system stored on the memory and operable on the processor, the knowledge base document The step of implementing the knowledge base document quick retrieval method as described above when the fast retrieval system is executed by the processor.
  • the present application further provides a computer readable storage medium storing a knowledge base document fast retrieval system, the knowledge base document fast retrieval system being configurable by at least one processor Executing to cause the at least one processor to perform the steps of the knowledge base document fast retrieval method as described above.
  • the knowledge database document fast retrieval method, the application server, and the computer readable storage medium proposed by the present application first receive retrieval information input by a user; secondly, analyze and process the retrieval information to obtain a query word. Searching the documents in the knowledge base again according to the query words, and sorting the search results according to the search matching degree; then obtaining the abstracts and keywords of each document through the summary generation model and the keyword generation model; Search results and corresponding to the summary and keywords of the output target document.
  • the knowledge database document fast retrieval method, the application server and the computer readable storage medium proposed in the present application the files in the knowledge base can be quickly and accurately retrieved, and the main contents of the retrieved files can be quickly understood.
  • 1 is a schematic diagram of an optional hardware architecture of an application server of the present application
  • FIG. 2 is a schematic diagram of a program module of an implementation manner of a quick retrieval system of the knowledge base document of the present application
  • FIG. 3 is a schematic flowchart of a first embodiment of a method for quickly searching a knowledge base of the present application
  • FIG. 4 is a schematic flowchart of a second embodiment of a method for quickly searching a knowledge base of the present application
  • FIG. 5 is a schematic flowchart of a third embodiment of a method for quickly searching a knowledge base of the present application
  • FIG. 6 is a schematic flowchart of a fourth embodiment of a method for quickly searching a knowledge base of the present application
  • FIG. 7 is a schematic flowchart of a fifth embodiment of a method for quickly searching a knowledge base of the present application.
  • FIG. 8 is a schematic flowchart of a sixth embodiment of a method for quickly searching a knowledge base of the present application
  • FIG. 9 is a schematic flowchart diagram of a seventh embodiment of a quick retrieval method of the knowledge base document of the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the application server 1 of the present application.
  • the application server 1 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus. It is pointed out that Figure 1 only shows the application server 1 with components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the application server 1 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the application server 1 may be an independent server or a server cluster composed of multiple servers. .
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static Random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 11 may be an internal storage unit of the application server 1, such as a hard disk or memory of the application server 1.
  • the memory 11 may also be an external storage device of the application server 1, such as a plug-in hard disk equipped on the application server 1, a smart memory card (SMC), and a secure digital number. (Secure Digital, SD) card, flash card, etc.
  • SMC smart memory card
  • SD Secure Digital
  • the memory 11 can also include both the internal storage unit of the application server 1 and its external storage device.
  • the memory 11 is generally used to store an operating system installed in the application server 1 and various types of application software, such as program code of the knowledge base document quick retrieval system 200. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments.
  • the processor 12 is typically used to control the overall operation of the application server 1.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running the knowledge base document fast retrieval system 200 and the like.
  • the network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the application server 1 and other electronic devices.
  • the present application proposes a knowledge base document rapid retrieval system 200.
  • FIG. 2 there is shown a block diagram of the first embodiment of the knowledge base document quick retrieval system 200 of the present application.
  • the knowledge base document rapid retrieval system 200 includes a series of computer program instructions stored on the memory 11, and when the computer program instructions are executed by the processor 12, the knowledge base document quick retrieval operation of the embodiments of the present application can be implemented.
  • the knowledge base document quick retrieval system 200 can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 2, the knowledge base document quick retrieval system 200 can be divided into an acquisition module 21, an analysis processing module 22, a retrieval module 23, a sorting module 24, an establishing module 25, a calling module 26, and an output module 27. among them:
  • the obtaining module 21 is configured to receive retrieval information input by a user.
  • the search information may be different according to different situations.
  • the search information may include the following three types: in the first case, the search information is a sentence; in the second case, the search information is a word.
  • the retrieval information includes the case of a sentence and a word.
  • the analysis processing module 22 is configured to analyze and process the search information to obtain a query word.
  • the first way for the case where the search information is a sentence, the input sentence is processed by word segmentation through the combination of grammar analysis and semantic analysis, the meaningless word symbols are eliminated, and several query words are extracted and transmitted to the retrieval module. search for. For example, if the user enters “How is the economic form of China this year?”, the key query words of “China” and “Economy” can be obtained through analysis, and unimportant word symbols such as auxiliary words, interrogative words and symbols are excluded. ;
  • the query word is conceptually extended into corresponding synonyms, synonyms and upper and lower words according to a preset rule, and part of the extended words or receiving users are extracted according to the synonym similarity similarity algorithm.
  • the selected extended word is used as the query word, and the choice of the extended word as the query word can be based on the priority level of each word. For example, the user enters "college students", and the subsequent "college students” can be extended to "undergraduate students", "graduate students”, “specialist students”, “college students”, “secondary students”, etc.:
  • the third way is to combine the two functions.
  • the specific combination process is as follows: firstly, the semantic analysis and the grammatical analysis are combined to process the word segmentation, and then the segmented query words are conceptually extended into corresponding synonyms and synonyms. Or the upper and lower words, according to the similarity priority algorithm, extract part of the extended words or receive the extended words selected by the user, and finally pass the query words together with the defined extended words as the query conditions to the retrieval module.
  • the searching module 23 is configured to search a document in the knowledge base according to the query word.
  • the documents in the knowledge base include various types, for example, text files including pdf, doc, docx, ppt, excel, txt, html, xml, zip, tar, and the like.
  • a full-text search operation can be performed, and a database is used as a source to build an index library, and the search matching degree is obtained by using TF-IDF to calculate the weight, and the search result is intelligently sorted according to the searched matching degree, and the search word is highlighted.
  • Search methods include cross-language information retrieval, spell check, regular search (for professionals), real-time search results, and entry records, etc., to achieve the optimal operation of assisted retrieval. Search results can be automatically completed based on historical records and hot searches during the search process.
  • the sorting module 24 is configured to sort the search results according to the search matching degree.
  • the establishing module 25 is configured to establish a summary generation model and a keyword generation model.
  • the calling module 26 is configured to invoke the summary generation model and the keyword generation model to obtain a summary and keywords of each document.
  • obtaining a summary and keywords of each document includes the following steps:
  • the target document is sentenced and segmented, and the content of the target document is split into sentences and words.
  • the digest generating model obtains a sentence generating digest whose weight value is greater than a preset value, and generates a keyword by using a keyword generating model to select a word with a word frequency greater than a preset value.
  • the output module 27 is configured to output the sorted search result, and corresponding to the summary and keywords of the output target document.
  • the user usually clicks on the top-ranked document for viewing.
  • the display module displays the content/summary/keyword of the document.
  • the present application also proposes a quick retrieval method of the knowledge base document.
  • FIG. 3 it is a schematic flowchart of the first embodiment of the quick retrieval method of the knowledge base document of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 3 may be changed according to different requirements, and some steps may be omitted.
  • Step S110 receiving retrieval information input by the user.
  • the search information may be different according to different situations.
  • the search information may include the following three types: in the first case, the search information is a sentence; in the second case, the search information is a word.
  • the retrieval information includes the case of a sentence and a word.
  • Step S120 analyzing and processing the search information to obtain a query word.
  • Step S130 Searching for documents in the knowledge base according to the query words, and sorting the search results according to the search matching degree.
  • the documents in the knowledge base include various types, for example, text files including pdf, doc, docx, ppt, excel, txt, html, xml, zip, tar, and the like.
  • a full-text search operation can be performed, and a database is used as a source to build an index library, and the search matching degree is obtained by using TF-IDF to calculate the weight, and the search result is intelligently sorted according to the searched matching degree, and the search word is highlighted.
  • Search methods include cross-language information retrieval, spell check, regular search (for professionals), real-time search results, and entry records, etc., to achieve the optimal operation of assisted retrieval. Search results can be automatically completed based on historical records and hot searches during the search process.
  • Step S140 obtaining a summary and a keyword of each document by using a digest generation model and a keyword generation model.
  • Step S150 outputting the sorted search result, and corresponding to the summary and keywords of the output target document.
  • step S120 in the first embodiment the method of analyzing and processing the search information to obtain a query word specifically includes the following steps:
  • search information when the search information is a sentence, the input sentence is subjected to word segmentation by a combination of syntax analysis and semantic analysis, the meaningless word symbol is removed, and a plurality of the query words are extracted.
  • the search information is a word
  • the word is conceptually expanded into corresponding synonyms, synonyms, and upper and lower words according to a preset rule, and part of the extended words or receiving user selection is extracted according to the synonym similarity similarity algorithm.
  • the extension word is used as the query term.
  • step S120 in the first embodiment “analysing and processing the search information to obtain a query word” specifically includes the following steps:
  • the system first divides the paragraph and the sentence into words, and after analysis, obtains more important words, and expands the important words into meanings, and the extended words include the upper words and the lower positions. Words, synonyms, synonyms, and so on. For example, if the user enters "How is the economic form of China this year?" The system obtains two query words “China” and “Economy”, then the system can obtain the extension words of "China”, such as “venue”, “venue” “Domestic”, etc.; according to the “economic”, the extension words “GDP”, "trade”, “commercial”, “financial”, “financial”, etc. are available.
  • step S130 in the first embodiment “searching for documents in the knowledge base according to the query words, and sorting the search results according to the search matching degree” specifically includes:
  • the retrieval methods include cross-language information retrieval, spell checking, regular retrieval (for professionals), real-time retrieval results, and entry records, etc., and the optimal operation of the auxiliary retrieval is realized.
  • TF-IDF is a statistical method used to assess the importance of a word for a file set or one of the files in a corpus. The importance of a word increases proportionally with the number of times it appears in the file, but it also decreases inversely with the frequency it appears in the corpus.
  • the main idea of TF-IDF is: If a word or phrase appears in an article with a high frequency TF and rarely appears in other articles, then the word or phrase is considered to have good class distinguishing ability and is suitable for use. To classify. TFIDF is actually: TF*IDF, TF word frequency (Term Frequency), IDF inverse file frequency (Inverse Document Frequency). TF indicates how often the term appears in the document.
  • a matching degree threshold may be set to display a document larger than the matching degree threshold.
  • the user can also display the number of documents on one interface as needed, for example, 20, 30, 50, and the like.
  • FIG. 7 is a schematic flowchart diagram of a fifth embodiment of the method for quickly searching for a knowledge base document of the present application.
  • "searching for documents in the knowledge base according to the query words, and sorting the search results according to the search matching degree" further includes the steps of:
  • the search result is automatically completed according to the historical record and the hot search.
  • the search results and the hot search can be used to supplement and optimize the search results, so that the search results are more complete and accurate.
  • the historical search record is stored in a database or a server, and the hot search result can also be obtained from the database or the retrieved record statistics of the server.
  • FIG. 8 is a schematic flowchart diagram of a sixth embodiment of a method for quickly searching for a knowledge base document of the present application.
  • step S140 in the first embodiment "the summary and keywords of each document are obtained by the digest generation model and the keyword generation model" specifically include:
  • S610 performing segmentation and word segmentation on the target document, and splitting the content of the target document into sentences and words;
  • S620 Generate, by using the digest generation model, a sentence whose weight value is greater than a preset value, generate the digest, and generate, by using the keyword generation model, a word whose word frequency is greater than a preset value to generate the keyword.
  • step S140 in the first embodiment "the summary and keywords of each document are obtained by the digest generation model and the keyword generation model" further includes:
  • Wi a*WPi+b*WSi
  • the keyword generation model is established based on word frequency statistics.
  • the weight value of each sentence of Wi is the weight of each sentence and each keyword
  • WPi is the position weight value
  • WSi is the semantic weight value
  • a and b are the weight coefficients
  • wp(ij) is the jth keyword
  • sp(j) is the number of sentences in each sentence containing the j-th keyword
  • m is the total number of sentences
  • n is the total number of keywords.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • a storage medium such as ROM/RAM, disk
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

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Abstract

La présente invention concerne un procédé de recherche rapide de document dans une base de connaissances, un serveur d'application et un support d'informations lisible par ordinateur, le procédé consistant : à recevoir des informations de recherche entrées par un utilisateur (S10); à analyser et à traiter les informations de recherche afin d'acquérir un mot d'interrogation (S120); sur la base du mot d'interrogation, à rechercher des documents dans une base de connaissances, et à classer les résultats de recherche sur la base d'un degré de correspondance de recherche (S130); au moyen d'un modèle de génération de résumés et d'un modèle de génération de mots clés, à acquérir un résumé et des mots clés de chaque document (S140); et à délivrer les résultats de recherche classés et à délivrer de manière correspondante le résumé et les mots-clés d'un document cible (S150). Le présent procédé de recherche rapide de document dans une base de connaissances, le serveur d'application et le support d'informations lisible par ordinateur permettent de rechercher rapidement et avec précision des documents dans une base de connaissances, et de comprendre rapidement le contenu principal des documents récupérés.
PCT/CN2018/077675 2017-11-10 2018-02-28 Procédé de recherche rapide de document dans une base de connaissances, serveur d'application, et support d'informations lisible par ordinateur WO2019091026A1 (fr)

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