WO2012130145A1 - Procédé et dispositif d'acquisition et de recherche d'informations de connaissance pertinentes - Google Patents

Procédé et dispositif d'acquisition et de recherche d'informations de connaissance pertinentes Download PDF

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Publication number
WO2012130145A1
WO2012130145A1 PCT/CN2012/073234 CN2012073234W WO2012130145A1 WO 2012130145 A1 WO2012130145 A1 WO 2012130145A1 CN 2012073234 W CN2012073234 W CN 2012073234W WO 2012130145 A1 WO2012130145 A1 WO 2012130145A1
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WO
WIPO (PCT)
Prior art keywords
query
question
hotspot
knowledge
page
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PCT/CN2012/073234
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English (en)
Chinese (zh)
Inventor
杨明
王源
唐曼华
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百度在线网络技术（北京）有限公司
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Priority to JP2014501426A priority Critical patent/JP5780617B2/ja
Publication of WO2012130145A1 publication Critical patent/WO2012130145A1/fr

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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
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/3331—Query processing
    • G06F16/3349—Reuse of stored results of previous queries
    • 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
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G06F16/9574—Browsing optimisation, e.g. caching or content distillation of access to content, e.g. by caching

Definitions

  • the present invention relates to the field of Internet communication technologies, and in particular, to a method and apparatus for acquiring and searching for related knowledge information.
  • the knowledge question answering system is a system that uses communication functions to achieve information acquisition. Users can submit various questions in the knowledge question answering system through the web page, query the status of the submitted questions, and decide which answer to use based on the status of the question answer. Other users can view the problem by visiting this page and answering according to their own preferences and knowledge.
  • the invention provides a method and device for acquiring and searching related knowledge information, so as to provide related knowledge information quickly and accurately.
  • a method for obtaining relevant knowledge information comprising:
  • step B Use the query excavated in step A to form a question and publish it on the page of the knowledge question answering platform;
  • the step A specifically includes:
  • a query having a question request is identified in the search log, and a hotspot query is determined in the search log, and the identified query with the question requirement and the determined hot spot query are taken together.
  • the query identifying the query request specifically includes:
  • the words obtained after the word segmentation are respectively matched in the question attribute database to determine the question propensity score of each word;
  • the query attribute database stores each word obtained by the data mining method or the manual configuration method and the question propensity score corresponding to each word.
  • the question propensity score corresponding to the word is determined by the following factors:
  • the words are interrogative words, or the relationship between words and interrogative words.
  • the determining the hotspot query may include:
  • the search frequency of each query in each query group is added to determine the search frequency of each query group
  • the query formation excavated by using step A in step B specifically includes:
  • the excavated query is subjected to semantic-based word segmentation, and the words are tagged;
  • the word-processed word is compared with the pre-set question-sentence grammar, and the missing word is added to the word-processed word, and assembled into a question satisfying the question-sentence grammar.
  • the posting of the question on the page of the knowledge question answering platform specifically includes:
  • the step C specifically includes:
  • the method further includes:
  • any relevant knowledge information of the question has not appeared, or the quality answer of the question has not yet appeared, then the knowledge is closed.
  • a method for searching related knowledge information is based on the foregoing method for acquiring related knowledge information, and the method for searching related knowledge information includes:
  • Searching for a page matching the keyword of the query wherein if a page matching the keyword of the query is searched on the knowledge question answering platform, matching the keyword of the query on the knowledge question answering platform The page is included in the search results of the query and returned to the user.
  • An apparatus for acquiring related knowledge information comprising: a search request query mining unit, a question forming unit, a question issuing unit, and a knowledge acquiring unit;
  • the query mining unit is configured to analyze a search log and mine a hotspot query with a questionable requirement
  • the question forming unit is configured to use the query excavated by the query mining unit to form a question
  • the question issuing unit is configured to post the question on a page of the knowledge question answering platform
  • the knowledge acquisition unit is configured to acquire related knowledge information of the question through a page of the knowledge question and answer platform.
  • the query mining unit specifically includes: a requirement identification subunit and a hotspot determination subunit;
  • the requirement identification subunit is configured to identify and output a query having a question request from the input query
  • the hotspot determining subunit is configured to determine and output a hotspot query from the input query
  • the input of the requirement identification subunit is a query in a search log
  • the input of the hotspot determination subunit is an output of the requirement identification subunit
  • the output of the hotspot determination subunit is the questionable requirement Hot spot query
  • the input of the hotspot determining subunit is a query of the search log
  • the input of the demand identifying subunit is an output of the hotspot determining subunit
  • the output of the demand identifying subunit is the hotspot query having the question demand
  • the input of the requirement identification subunit is a query in the search log
  • the input of the hotspot determination subunit is also a query in the search log
  • the device further includes: an intersection processing subunit, configured to determine the hot spot
  • the subunit and the requirement identification subunit take an intersection and output a hotspot query with a questionable requirement.
  • the requirement identification subunit specifically includes: a word segmentation processing module, a word scoring module, a query scoring module, and a requirement judging module;
  • the word segmentation processing module is configured to perform semantic-based word segmentation processing on the input query
  • the word scoring module is configured to match each word obtained after the word segmentation processing in a question attribute database, and determine a question propensity score of each word;
  • the query scoring module is configured to add the question propensity scores of the words to obtain a question propensity score of the input query;
  • the requirement judging module is configured to determine whether the interrogation tendency score of the input query exceeds a preset question requirement threshold, and if yes, determine that the input query has a question requirement; otherwise, the input query is determined not to be Questionable demand;
  • the query attribute database stores each word obtained by the data mining method or the manual configuration method and the question propensity score corresponding to each word.
  • the question propensity score corresponding to the word is determined by the following factors:
  • the words are interrogative words, or the relationship between words and interrogative words.
  • the hotspot determining subunit specifically includes: a clustering processing module, a frequency statistics module, a hotspot group determining module, and a hotspot query determining module;
  • the clustering processing module is configured to perform correlation-based clustering on the query to obtain each query group;
  • the frequency statistics module is configured to add search frequencies of each query in each query group to determine a search frequency of each query group;
  • the hotspot group determining module is configured to determine a query group whose search frequency exceeds a preset hotspot frequency as a hotspot query group;
  • the hotspot query determining module is configured to select a query from each hotspot query group as a hotspot query.
  • the question forming unit may include: a part-of-speech identifier sub-unit and a sentence assembly sub-unit;
  • the part-of-speech identifier sub-unit is configured to perform a semantic-based word segmentation process on the query excavated by the query mining unit, and put a part-of-speech tag;
  • the sentence assembly sub-unit is configured to compare the word-processed word with a pre-set question-sentence grammar according to the part-of-speech tag, add a missing word to the word-processed word, and assemble the satisfied word Ask questions about the sentence grammar.
  • the question issuing unit specifically selects an ID from a preset set of simulated question IDs, and uses the selected ID to simulate that the user forms a question formed by the question forming unit on a page of the knowledge question answering platform;
  • the ID in the set of simulated question IDs is defaulted to the ID of the registered user by the knowledge question answering platform.
  • the knowledge acquisition unit specifically obtains relevant knowledge information that is answered by the user for the question and answers from the page of the knowledge question answering platform, and determines a high quality answer from the related knowledge information.
  • the device further includes:
  • a page maintenance unit configured to: when the posting duration of the question on the page of the knowledge question answering platform reaches a preset closing duration, if any relevant knowledge information of the question has not appeared, or the quality of the question has not yet appeared The answer is to close the page where the question is located on the knowledge quiz platform.
  • An apparatus for searching related knowledge information comprising: the foregoing apparatus for acquiring related knowledge information, a user interaction unit, and a page search unit;
  • the user interaction unit is configured to receive a query input by a user
  • the page search unit is configured to search for a page that matches the keyword of the query, and if the device that obtains the relevant knowledge information from the above is posted on the knowledge question answering platform, the key of the query is searched. If the word matches the page, the searched page is included in the search result of the query and returned to the user.
  • the present invention mines the hotspot query with questioning requirements by analyzing the search log, and uses the excavated query to form a question and publish it on the page of the knowledge question answering platform, so that the user has relevant question when the search engine
  • the page on which the question is located on the knowledge quiz platform can be returned to the user, so that the user can obtain relevant knowledge information of the question from the page. That is to say, through the invention, the relevant knowledge information existing on the knowledge question answering platform can be quickly and accurately provided by the search engine, and the user does not have to log in to the knowledge question answering platform to issue a question, and waits for the question to be answered before the relevant knowledge information can be obtained.
  • FIG. 1 is a flowchart of a method for acquiring related knowledge information according to Embodiment 1 of the present invention
  • FIG. 2 is a flowchart of a method for determining a hotspot query according to Embodiment 2 of the present invention
  • Embodiment 3 is a flowchart of a method for searching for related knowledge information according to Embodiment 3 of the present invention.
  • FIG. 4 is a structural diagram of an apparatus for acquiring related knowledge information according to Embodiment 4 of the present invention.
  • Figure 5 (a), (b) and (c) are three structural diagrams of the query mining unit provided in the fourth embodiment of the present invention.
  • FIG. 6 is a structural diagram of a requirement identification subunit provided by Embodiment 4 of the present invention.
  • FIG. 7 is a structural diagram of a hotspot determining subunit provided by Embodiment 4 of the present invention.
  • FIG. 8 is a structural diagram of an apparatus for searching for related knowledge information according to Embodiment 5 of the present invention.
  • FIG. 1 is a flowchart of a method for acquiring related knowledge information according to Embodiment 1 of the present invention. As shown in FIG. 1 , the method may include the following steps:
  • Step 101 Analyze the search log and mine a hotspot query with questionable requirements.
  • the search log can be periodically analyzed to capture the search log in the current period; then the search log in the current period of the crawl is used to mine the hotspot query with the question.
  • the period for analyzing the search log can be flexibly set. For example, the hotspot query with the doubtful demand is extracted from the search log of the day in a daily cycle.
  • This step is actually divided into two parts: one part is to identify whether the query in the search log has a questionable requirement; the other part is to determine the hotspot query.
  • the operations of these two parts can be performed in any order, or they can be executed in parallel, and finally the hotspot query with doubtful requirements is mined. That is, you can first identify the query with doubtful requirements in the search log, and then determine the hotspot query in the query with questioning requirements; you can also determine the hotspot query first, and then identify the query with the question in the hot query; Synchronize the query and hotspot query with doubtful requirements, and then take the intersection of the two.
  • the process of identifying whether the query has a questionable requirement may include: performing semantic-based word segmentation processing on the query, matching each word obtained after the word segmentation processing in the question attribute database, and determining the question propensity score of each word; The interrogative tendency scores of the words are added to obtain the questioning tendency score of the query; if the questioning tendency score of the query exceeds the preset question demand threshold, it is determined that the query has a questionable demand; otherwise, the query is determined to have no doubt demand.
  • the query attribute database stores the words obtained by the data mining method or the manual configuration method and the corresponding question tendency scores.
  • the question propensity score corresponding to each word in the question attribute database may be determined by, but not limited to, whether the word is an interrogative word, an association relationship between the word and the question word. For example, for question words such as "what”, “what”, “how”, “how”, “why”, etc., you can set the highest question propensity score; for words that are often used as the context of question words, such as "practice”, “Method”, “method”, etc. can be considered to have a strong correlation with the interrogative words, and can set a higher questioning tendency score; for other words with less interrogative words, a smaller questioning tendency can be set. Score.
  • the "query” query after the semantic-based word segmentation process, obtains the words “fishy shredded pork” and "practice". After matching these two words in the question attribute database, it is determined that "fishy pork" "There is no matching word in the question attribute database, and the question propensity score is 0. After the "practice” is matched in the question attribute database, the question propensity score is determined to be 70. After adding, it is determined that the question's questioning tendency score is 70. If the set question demand threshold is 60 points, the query may be considered to have a questionable demand.
  • the hot query that is finally discovered and has a questionable requirement can be stored as a file in the database.
  • Step 102 The question is generated by using the excavated query and posted on the page of the knowledge question answering platform.
  • the excavated query can be separately analyzed and assembled based on semantics to form a question.
  • the excavated query is subjected to semantic-based word segmentation and words are tagged with part-of-speech tags.
  • these words are compared with the pre-set question sentence grammar, and the missing words are added to form a question that satisfies the grammar of the question sentence.
  • the question sentence grammar can be flexibly set, as long as the requirements of the commonly used question syntax are met.
  • the sentence syntax for setting a question is: [adjective/noun + function word] + noun + verb + question auxiliary word + question symbol, where [] indicates an option. If the words obtained by a query after word segmentation are nouns and verbs, you can fill in the appropriate interrogative auxiliary and question symbols, and finally assemble the questions.
  • the word “fishy shredded pork” is tagged with nouns, the “practice” is tagged with nouns, and then compared with the pre-defined question sentence grammar.
  • the wording of the word, the interrogative auxiliary and the symbol, the question that can be formed can be "how is the practice of fish-flavored pork?" ".
  • the knowledge quiz platform manages the registered users by ID.
  • the simulated question ID set can be preset in advance.
  • the IDs in the ID set are all defaulted by the knowledge quiz platform as the ID of the registered user.
  • the ID can be selected from the preset set of simulated question IDs. Unused IDs are published to challenge the registered users on the Knowledge Q&A platform to ask questions.
  • the questions involved in the present invention are not limited to common problems, and may be applied to other forms of questions, for example, may be a question asking for a document, and the related knowledge information of the question may be a document uploaded by another user. .
  • Step 103 Obtain relevant knowledge information of the question through a page on the knowledge question answering platform.
  • the registered user on the knowledge quiz platform answers the question page to provide relevant knowledge information.
  • the high-quality answer can be determined in the relevant knowledge information answered on the page, wherein the high-quality answer can be determined by the administrator of the knowledge question answering platform, or can be automatically determined by the knowledge question answering platform according to the preset high-quality answer selection strategy.
  • the high-quality answer selection strategy may be determined by one or any combination of the following factors: the user level that answers the question, the adoption rate of the question answered by the user, and the length of the related knowledge information.
  • FIG. 2 is a flowchart of determining a hotspot query according to Embodiment 2 of the present invention. As shown in FIG. 2, the process may include the following steps:
  • Step 201 Perform correlation-based clustering on the query to obtain each query group.
  • the clustering object of this step is: the crawled search The query in the log.
  • the cluster object of this step is: the query with the question requirement identified in the search log.
  • each query contained in each query group has a high correlation. For example, the correlation between the “World Expo”, “Expo” and “Expo” is very high, and the cluster is satisfied. If required, these queries are clustered into a single query.
  • Step 202 Add the search frequency of each query in the query group to determine the search frequency of the entire query group.
  • the search frequency of each query can be counted, and the search frequency of each query in each query group is added, which can be used as the search frequency of the entire query group, reflecting the heat of the entire query group.
  • Step 203 Determine that the search frequency of the query group exceeds the preset hotspot frequency. If yes, execute step 204; otherwise, determine that the query group is not a hotspot query group.
  • the search frequency of “World Expo” is 10,000 times within the set time
  • the search frequency of “World Expo” within the set time is 20,000 times
  • the "Expo" search frequency is 30,000 times in the set time
  • the search frequency corresponding to the set time of the entire query group is 60,000 times. If the preset hotspot frequency is 50,000 times, it can be determined that the query group is a hotspot query group.
  • Step 204 Determine that the query group is a hotspot query group, and select a query from the hotspot query group as a hotspot query.
  • the strategy of selecting a hotspot query from the hot query group may include, but is not limited to, the following strategies: selecting the query with the highest search frequency, selecting any query, selecting the query with the best semantic integrity, and the like.
  • FIG. 3 is a flowchart of a method for searching for related knowledge information according to Embodiment 3 of the present invention. As shown in FIG. 3, the method for searching related knowledge information may include the following steps:
  • Step 301 Receive a query input by a user.
  • Step 302 Search for a page that matches the keyword of the query; wherein if the page on the knowledge quiz platform that matches the keyword of the query is searched, then the knowledge quiz platform and the query are The matching keywords of the keywords are included in the search results of the query and returned to the user.
  • the search engine when the search engine receives the query of the user input sent by the browser, when the search page is searched according to the query input by the user, the background has already simulated the user's question and posted in the process shown in FIG. 1 in advance.
  • the search engine searches for a page matching the keyword of the query from the captured page, it can match the page on the knowledge quiz platform that matches the keyword of the query, The page already contains relevant questions and relevant knowledge information for answering the questions.
  • the search engine can Quickly and accurately feedback relevant knowledge information already available on the knowledge question and answer platform in the search results.
  • the page of the knowledge quiz platform can be specially processed, that is, the search engine is allowed to capture the page that already has a good answer on the knowledge question answering platform, that is, if there is no high-quality answer on the question page of the knowledge question answering platform, the feedback is The question page will not be included in the search results for the user.
  • the apparatus may include: a query mining unit 400, a question forming unit 410, a question issuing unit 420, and a knowledge acquiring unit 430.
  • the query mining unit 400 is configured to analyze the search log and mine a hotspot query with questionable requirements.
  • the search log analyzed by the query mining unit 400 may be a search log periodically captured.
  • the question forming unit 410 is configured to form a question by using the hotspot query excavated by the query mining unit 400.
  • the question issuing unit 420 is configured to post the question on the page of the knowledge question answering platform.
  • the knowledge acquisition unit 430 is configured to obtain related knowledge information of the question through the page of the knowledge question answering platform.
  • the question issuing unit 420 and the knowledge obtaining unit 430 may be units independent of the knowledge question answering platform, or may be units arranged in the knowledge question answering platform.
  • the structure of the query mining unit 400 may be as shown in FIG. 5, and specifically includes a requirement identification subunit 401 and a hotspot determination subunit 402.
  • the requirement identification sub-unit 401 is configured to identify and output a query with a question request from the input query.
  • the hotspot determination subunit 402 is configured to determine and output a hotspot query from the input query.
  • the input of the demand identification subunit 401 may be the query in the captured search log, and the input of the hotspot determination subunit 402 is the output of the demand identification subunit 401. At this time, the output of the hotspot determination subunit 402 is in doubt. Hot spot query for demand.
  • the connection relationship between the demand identification sub-unit 401 and the hot spot determination sub-unit 402 in this case is as shown in (a) of FIG.
  • the input of the hotspot determination subunit 402 is the query of the search log
  • the input of the demand identification subunit 401 is the output of the hotspot determination subunit 402.
  • the output of the requirement identification subunit 401 is the hotspot query with the question demand.
  • the connection relationship between the demand identification sub-unit 401 and the hotspot determination sub-unit 402 in this case is as shown in (b) of FIG.
  • the input of the requirement identification sub-unit 401 is the query in the captured search log
  • the input of the hotspot determination sub-unit 402 is also the query in the captured search log, in which case the requirement identification sub-unit 401 and
  • the connection relationship of the hotspot determination sub-unit 402 is as shown in (c) of FIG. 5, and the apparatus may further include a sub-unit that intersects the hot spot determination sub-unit 402 and the demand identification sub-unit 401, that is, in FIG. 5 ( c) The intersection processing sub-unit 403 shown, whose output is a hotspot query with questionable requirements.
  • the structure of the requirement identification sub-unit 401 can be as shown in FIG. 6, and specifically includes: a word segmentation processing module 601, a word scoring module 602, a query scoring module 603, and a requirement judging module 604.
  • the word segmentation processing module 601 is configured to perform semantic-based word segmentation processing on the input query.
  • the word scoring module 602 is configured to match each word after the word segmentation in the question attribute database to determine the question propensity score of each word.
  • the question attribute database stores each word obtained by the data mining method or the manual configuration method and the question propensity score corresponding to each word.
  • the query scoring module 603 is configured to add the interrogation tendency scores of the words to obtain the interrogation tendency score of the input query.
  • the requirement judging module 604 is configured to determine whether the interrogation score of the input query exceeds a preset question demand threshold, and if so, determine that the entered query has a question requirement; otherwise, the input query is determined to have no doubt demand.
  • the question propensity score corresponding to the above words may be determined by, but not limited to, the following factors: whether the words are interrogative words, or the relationship between the words and the interrogative words.
  • the structure of the hotspot determining sub-unit 402 may be as shown in FIG. 7, and specifically includes: a clustering processing module 701, a frequency statistics module 702, a hotspot group determining module 703, and a hotspot query determining module 704.
  • the clustering processing module 701 is configured to perform correlation-based clustering on the query to obtain each query group.
  • the frequency statistics module 702 is configured to add the search frequency of each query in each query group to determine the search frequency of each query group.
  • the hotspot group determining module 703 is configured to determine the query group whose search frequency exceeds the preset hotspot frequency as the hotspot query group.
  • the hotspot query determining module 704 is configured to select a query from each hotspot query group as a hotspot query.
  • the strategy for selecting a hotspot query from the hotspot query group may include, but is not limited to, selecting the query with the highest search frequency, selecting any query, or selecting the query with the best semantic integrity.
  • the question forming unit 410 may specifically include a part-of-speech identifier sub-unit 411 and a sentence assembly sub-unit 412.
  • the part-of-speech tag sub-unit 411 is configured to perform a semantic-based word segmentation process on the hotspot query excavated by the query mining unit 400, and tag the word tag.
  • the part-of-speech tag sub-unit 411 may itself have the function of word segmentation processing, that is, the part-of-speech tag sub-unit 411 first performs semantic-based word segmentation processing on the hotspot query excavated by the query mining unit 400, and then puts the words obtained by the word segmentation into words. label.
  • the part-of-speech tag sub-unit 411 may not have the function of word segmentation processing, and directly utilizes the word segmentation processing result of the hotspot query in the word segmentation processing module 601 in the requirement identification sub-unit 401, and puts the word obtained after the word segmentation process into a part-of-speech tag.
  • the sentence assembly sub-unit 412 is configured to compare the words obtained after the word segmentation with the pre-set question sentence grammar according to the part-of-speech tag, add the missing words to the word-processed words, and assemble the questions into the question grammar. .
  • the knowledge question answering platform manages the registered users by ID.
  • the simulated question ID set can be set in advance, and the simulated question ID set is simulated.
  • the ID is defaulted to the ID of the registered user by the knowledge quiz platform.
  • the question issuing unit 420 at this time may select one ID from the set of simulated question IDs set in advance, and simulate the question formed by the question forming unit 410 by the user using the selected ID to be posted on the page of the knowledge question answering platform.
  • the knowledge obtaining unit 430 can obtain relevant knowledge information of the answering user's answer to the question from the page of the knowledge question answering platform, and further determine the high quality answer from the related knowledge information.
  • the high-quality answer may be determined by the administrator's participation, or may be determined by the knowledge question answering platform according to one or a combination of the user level of the answering question, the question adoption rate of the user who answers the question, and the length of the related knowledge information.
  • the device further includes: a page maintenance unit 440, configured to advance the time of posting on the page of the knowledge question answering platform.
  • a page maintenance unit 440 configured to advance the time of posting on the page of the knowledge question answering platform.
  • FIG. 8 is a structural diagram of an apparatus for searching for related knowledge information according to Embodiment 5 of the present invention.
  • the apparatus includes: the apparatus shown in FIG. 4, a user interaction unit 801, and a page search unit 802.
  • the user interaction unit 801 is configured to receive a query input by a user.
  • the page search unit 802 is configured to search for a page that matches the keyword of the query. If the device shown in FIG. 4 searches for a page matching the query keyword on the page where the question is posted on the knowledge question answering platform, The searched page is included in the search results of the query and returned to the user.
  • the page crawled by the search engine also contains the page on the knowledge quiz platform.
  • the page of the knowledge question answering platform can be specially processed, that is, the page searching unit 802 is allowed to search for a page that already has a good answer on the knowledge question answering platform. If there is no good answer in the question page of the knowledge question answering platform, then return to the page. The user's search results will not include the question page, that is, the search engine can not capture the page on the knowledge quiz platform for the question that has not yet appeared a good answer.

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Abstract

La présente invention concerne un procédé et un dispositif permettant d'acquérir et de rechercher des informations de connaissance pertinentes. Le procédé consiste à : analyser un journal de recherche, à exploiter une demande de recherche de point chaud avec une exigence de doute; former une question à l'aide de la demande exploitée et afficher la question sur une page d'une plateforme de questions et réponses de connaissance; et acquérir des informations de connaissance pertinentes sur la question par le biais de la page de la plateforme de questions et réponses de connaissance. Lors de la réception d'une demande entrée par un utilisateur, le procédé recherche une page correspondant à un mot-clé de la demande. Si une page correspondant au mot-clé de la demande est trouvée sur la plateforme de questions et réponses de connaissance, la page correspondant au mot-clé de la question sur la plateforme de questions et réponses de connaissance est incluse dans un résultat de recherche de la question et renvoyée à l'utilisateur. La présente invention permet de fournir rapidement et avec précision des informations de connaissance pertinentes à un utilisateur et l'utilisateur ne doit pas se connecter à une plateforme de questions et réponses de connaissance pour émettre une question et acquérir des informations de connaissance pertinentes jusqu'à ce qu'il soit répondu à la question.
PCT/CN2012/073234 2011-03-31 2012-03-29 Procédé et dispositif d'acquisition et de recherche d'informations de connaissance pertinentes WO2012130145A1 (fr)

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