WO2017173773A1 - 信息搜索方法和装置 - Google Patents

信息搜索方法和装置 Download PDF

Info

Publication number
WO2017173773A1
WO2017173773A1 PCT/CN2016/097291 CN2016097291W WO2017173773A1 WO 2017173773 A1 WO2017173773 A1 WO 2017173773A1 CN 2016097291 W CN2016097291 W CN 2016097291W WO 2017173773 A1 WO2017173773 A1 WO 2017173773A1
Authority
WO
WIPO (PCT)
Prior art keywords
search
requirement
knowledge entity
entity
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2016/097291
Other languages
English (en)
French (fr)
Chinese (zh)
Inventor
秦首科
黄飞
陈志扬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Baidu Netcom Science and Technology Co Ltd
Original Assignee
Beijing Baidu Netcom Science and Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Baidu Netcom Science and Technology Co Ltd filed Critical Beijing Baidu Netcom Science and Technology Co Ltd
Priority to KR1020187032155A priority Critical patent/KR102148691B1/ko
Priority to JP2018552167A priority patent/JP6732938B2/ja
Publication of WO2017173773A1 publication Critical patent/WO2017173773A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/35—Clustering; Classification
    • G06F16/353—Clustering; Classification into predefined classes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/35—Clustering; Classification
    • G06F16/355—Creation or modification of classes or clusters
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • the present application relates to the field of computer technologies, and in particular, to the field of search technologies, and in particular, to an information search method and apparatus.
  • the existing information retrieval technology generally searches a webpage containing content related to a search sentence according to a search sentence input by a user, extracts a content summary related to the search sentence in the webpage, and displays the digest in the search result.
  • the purpose of the present application is to propose an information search method and apparatus to solve the technical problems mentioned in the above background art.
  • the present application provides an information search method, the method comprising: receiving a search request, the search request including a search sentence input by a user; acquiring user information of the user, and based on the search sentence and the The user information is obtained by using a pre-trained classification model, wherein the search requirement includes: a knowledge entity, at least one requirement associated with the knowledge entity; and obtaining in a pre-stored knowledge entity information set At least one attribute information of the knowledge entity, wherein the at least one attribute information is in one-to-one correspondence with the at least one requirement; and the acquired attribute information is combined into one search result and added to the search result page.
  • the obtaining a search requirement of the user by using a pre-trained classification model based on the search statement and the user information comprises: pre-training based on the search statement and the user information a knowledge entity classification model, the knowledge entity is obtained; and the at least one requirement is obtained through a pre-trained demand classification model based on the search sentence, the user information, and the knowledge entity.
  • the method further comprises: after receiving the search request, obtaining an initial match with the search statement by a multi-pattern matching algorithm and according to a correspondence between the predetermined matching result and the initial knowledge entity and the initial requirement a knowledge entity and an initial requirement; and the obtaining, by the pre-trained knowledge entity classification model based on the search statement and the user information, the knowledge entity, including: based on the search statement, the user information, The initial knowledge entity and the initial requirement are obtained by a pre-trained knowledge entity classification model.
  • the obtaining, by the pre-trained demand classification model, the at least one requirement based on the search statement, the user information, and the knowledge entity including: based on the search statement, the user
  • the information, the knowledge entity, the initial knowledge entity, and the initial requirement are obtained by the pre-trained demand classification model.
  • the method further comprises: after receiving the search request, obtaining an entity word and a requirement word in the search sentence by a named entity recognition algorithm based on the search statement; and the searching based on the search
  • the statement, the user information, the initial knowledge entity, and the initial requirement are obtained by a pre-trained knowledge entity classification model, including: based on the search statement, the user information, the initial knowledge The entity, the initial requirement, the entity word, and the requirement word are obtained by a pre-trained knowledge entity classification model.
  • the obtaining, by the pre-trained demand classification model, the at least one requirement based on the search statement, the user information, and the knowledge entity including: based on the search statement, the user
  • the information, the knowledge entity, the initial knowledge entity, the initial requirement, the entity word, and the requirement word are obtained by the pre-trained demand classification model to obtain the at least one requirement.
  • the acquired attribute information includes at least one of the following: a graph Piece information, text information.
  • the present application provides an information search apparatus, the apparatus comprising: a search request receiving unit, configured to receive a search request, the search request including a search sentence input by a user; and a search requirement acquisition unit, configured to acquire Deriving user information of the user, and obtaining a search requirement of the user by using a pre-trained classification model based on the search statement and the user information, wherein the search requirement comprises: a knowledge entity, associated with the knowledge entity At least one requirement; the attribute information obtaining unit is configured to acquire at least one attribute information of the knowledge entity in the pre-stored knowledge entity information set, wherein the at least one attribute information is in one-to-one correspondence with the at least one requirement; The generating unit is configured to merge the obtained attribute information into one search result and join the search result page.
  • a search request receiving unit configured to receive a search request, the search request including a search sentence input by a user
  • a search requirement acquisition unit configured to acquire Deriving user information of the user, and obtaining a search requirement of the user by using
  • the search requirement acquisition unit includes: a knowledge entity acquisition subunit, configured to obtain the knowledge entity by using a pre-trained knowledge entity classification model based on the search sentence and the user information; a subunit, configured to obtain the at least one requirement by using a pre-trained demand classification model based on the search statement, the user information, and the knowledge entity.
  • the apparatus further includes: a multi-pattern matching unit, configured to obtain, after receiving the search request, a multi-pattern matching algorithm, and according to a correspondence between the predetermined matching result and the initial knowledge entity and the initial requirement, The initial knowledge entity and initial requirement that the search statement matches; and the knowledge entity acquisition subunit is further configured to pre-train based on the search statement, the user information, the initial knowledge entity, and the initial requirement
  • the knowledge entity classification model obtains the knowledge entity.
  • the requirement acquisition subunit is further configured to pass a pre-trained demand classification model based on the search statement, the user information, the knowledge entity, the initial knowledge entity, and the initial requirement, The at least one requirement is obtained.
  • the apparatus further includes: a named entity identifying unit, configured to obtain an entity word and a requirement word in the search sentence by a named entity recognition algorithm based on the search statement after receiving the search request; And the knowledge entity obtaining subunit is further configured to classify the pre-trained knowledge entity based on the search sentence, the user information, the initial knowledge entity, the initial requirement, the entity word, and the requirement word Model to get the knowledge entity.
  • a named entity identifying unit configured to obtain an entity word and a requirement word in the search sentence by a named entity recognition algorithm based on the search statement after receiving the search request
  • the knowledge entity obtaining subunit is further configured to classify the pre-trained knowledge entity based on the search sentence, the user information, the initial knowledge entity, the initial requirement, the entity word, and the requirement word Model to get the knowledge entity.
  • the requirement acquisition subunit is further configured to be based on the search statement, the user information, the knowledge entity, the initial knowledge entity, the initial requirement, the entity word, and the requirement The word, the at least one requirement is obtained by a pre-trained demand classification model.
  • the attribute information acquired by the attribute information acquiring unit includes at least one of the following: picture information, text information.
  • the information searching method and apparatus obtains a knowledge entity and at least one requirement in a user's search requirement through a pre-trained classification model based on the search sentence and the user information, and acquires in the pre-stored knowledge entity information set. Having at least one attribute information corresponding to the requirement of the knowledge entity, and combining the acquired attribute information into a search result to join the search result page, displaying the content required by the user, and enriching the display content of the search result .
  • FIG. 1 is an exemplary system architecture diagram to which the present application can be applied;
  • FIG. 2 is a flow chart of one embodiment of an information search method in accordance with the present application.
  • FIG. 3 is an exemplary schematic diagram of an application scenario of an information search method according to the present application.
  • FIG. 4 is a flow chart of another embodiment of an information search method according to the present application.
  • FIG. 5 is a schematic structural diagram of an embodiment of an information search apparatus according to the present application.
  • FIG. 6 is a block diagram showing the structure of a computer system suitable for implementing the server of the embodiment of the present application.
  • FIG. 1 illustrates an exemplary system architecture 100 in which an embodiment of the information search method or information search device of the present application may be applied.
  • system architecture 100 can include terminal devices 101, 102, 103, network 104, and server 105.
  • the network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105.
  • Network 104 may include various types of connections, such as wired, wireless communication links, fiber optic cables, and the like.
  • the user can interact with the server 105 over the network 104 using the terminal devices 101, 102, 103 to receive or transmit messages and the like.
  • Various client applications such as a browser application, a search application, a shopping application, and the like, may be installed on the terminal devices 101, 102, and 103.
  • the terminal devices 101, 102, 103 may be various electronic devices that support browser applications or search-type applications, including but not limited to smartphones, tablets, laptop portable computers, desktop computers, and the like.
  • the server 105 may be a server that provides various services, such as a database server or a cloud server that provides support for browser applications on the terminal devices 101, 102, 103, search applications, and the like.
  • the server can analyze, retrieve, and the like the received data, and feed back the processing result (for example, the search result) to the terminal device.
  • the information search method provided by the embodiment of the present application is generally performed by the server 105. Accordingly, the information search device is typically disposed in the server 105.
  • terminal devices, networks, and servers in Figure 1 is merely illustrative. Depending on the implementation needs, there can be any number of terminal devices, networks, and servers.
  • FIG. 2 illustrates a flow 200 of one embodiment of an information search method in accordance with the present application.
  • the information search method of this embodiment includes the following steps:
  • Step 201 Receive a search request.
  • the above search request includes a search sentence input by a user.
  • the electronic device for example, the server shown in FIG. 1
  • the electronic device on which the information search method runs may be from the terminal by wire or wirelessly (for example, as shown in FIG. 1
  • the terminal device receives the above search request.
  • the search sentence may be text input by a user in a search text box of a browser search page or a search application.
  • Step 202 Acquire user information of the user, and obtain a search requirement of the user by using a pre-trained classification model based on the search statement and the user information.
  • the foregoing search requirements include: a knowledge entity, and at least one requirement associated with the knowledge entity.
  • the electronic device may first obtain the user information of the user from the cookie information of the terminal, and may also obtain the user information of the user from the user image data that is pre-established.
  • the user information may include, but is not limited to, one or more of the following information: historical search record, gender, age, occupation, interest, and the like.
  • a pre-trained classification model for example, a logistic regression model
  • a classification algorithm for example, a logistic regression algorithm
  • the above knowledge entity may be an object corresponding to a knowledge point/proper noun (for example, “Jiuzhaigou” or “arthritis”).
  • the requirements associated with each of the predetermined knowledge entities may be preset by humans based on domain knowledge or by machine learning.
  • the requirements associated with the knowledge entity "arthritis” may include: “treatment”, “inquiry”, “understanding”.
  • the above classification model can be obtained by learning and training a large number of training data with physical annotations.
  • the training data may include the following information: a search term, a user information, a knowledge entity, and an annotation, wherein the annotation is used to indicate whether the search term is related to the knowledge entity of the training data.
  • the above training data can be collected by the user clicking on the content of the page entered by the search result and the search term used in the search. For example, suppose that the page that the user clicks on a certain search result is about Jiuzhaigou, then a training data can be generated, and the search word of the training data is the search word used by the user, and the knowledge entity is “Jiuzhaigou”, marked as 1 .
  • Step 203 Acquire at least one attribute information of the foregoing knowledge entity in the pre-stored knowledge entity information set.
  • the at least one attribute information is in one-to-one correspondence with the at least one requirement.
  • the knowledge entity information set includes multiple attribute information of each knowledge entity, and may include, for example, attribute information such as a strategy, introduction, history, and travel route of the knowledge entity “Jiuzhaigou”.
  • the electronic device may search for the knowledge entity information that matches the knowledge entity in the search requirement obtained in step 202 in the knowledge entity information set, and then obtain at least one of the above-mentioned search requirements corresponding to the at least one of the search requirements in the knowledge entity information.
  • An attribute information is a strategy, introduction, history, and travel route of the knowledge entity “Jiuzhaigou”.
  • the above-mentioned knowledge entity information set may be obtained in advance by crawling the edited structured information from a third-party site (for example, an encyclopedic site or a medical site). For example, for a knowledge entity "face”, information can be crawled from the relevant page of the predetermined medical site to obtain attribute information such as introduction, symptom, and cause.
  • a third-party site for example, an encyclopedic site or a medical site.
  • the acquired attribute information may include at least one of the following: picture information and text information.
  • Step 204 Combine the acquired attribute information into a search result and join the search result page.
  • the electronic device may combine at least one attribute information obtained in step 203 as a search result, and add the search result to the search result page, so that the terminal can display the search result including the plurality of attribute information.
  • the step 202 may include: obtaining, by using the pre-trained knowledge entity classification model, the knowledge entity based on the search statement and the user information; and based on the search statement, the user information, and The above knowledge entity obtains at least one of the above requirements through a pre-trained demand classification model.
  • the electronic device may input the above-mentioned search statement and the acquired user information into a pre-trained knowledge entity classification model, and obtain a probability corresponding to each knowledge entity by using a classification algorithm, and take the knowledge entity with the highest probability of correspondence as the search requirement.
  • Knowledge entity The training method of the knowledge entity classification model may refer to the training method of the classification model in step 202, and details are not described herein again.
  • the electronic device may input the above-mentioned search sentence, the user information, and the knowledge entity in the search requirement into the pre-trained demand classification model to obtain the probability corresponding to each requirement, and select the predetermined ones according to the corresponding probability from the largest to the smallest.
  • the number of requirements as at least one of the above search requirements.
  • the above demand classification model can be obtained by learning and training a large number of training data with demand labels.
  • the training data may include the following information: a search term, a user information, a knowledge entity, a requirement, and an annotation, wherein the annotation is used to indicate whether the search term is related to the knowledge entity and the requirement of the training data.
  • the above training data can be collected by the user clicking on the content of the page entered by the search result and the search term used in the search. For example, suppose that the page that the user clicks on a certain search result is about the travel strategy of Jiuzhaigou, then a training data can be generated, the search word of the training data is the search word used by the user, and the knowledge entity is “Jiuzhaigou”.
  • the demand is “Travel Guide” and is marked as 1.
  • At least one requirement associated with the knowledge entity in the obtained user's retrieval requirements is made more accurate, so that the content required by the user can be more accurately displayed.
  • FIG. 3 shows an exemplary schematic diagram of an application scenario of the information search method of this embodiment.
  • the user first enters the search term "face” in the search input box and clicks the search button.
  • the server obtains the search statement “face” in the search request, and obtains the search requirement of the user by using the information search method provided in this embodiment: the knowledge entity “face” and the knowledge entity “face” Associated “causes", “introduction”, “symptoms”; then obtain the attribute information of "face” which is corresponding to "cause", "introduction” and “symptom” in the knowledge entity information set, and the attribute information
  • the combination adds a search result page to a search result, and then sends the search result page to the terminal, and the terminal interface will display the graphic information about the cause, introduction, and symptom of the "face” as shown in FIG.
  • the information search method provided by the embodiment obtains the knowledge entity and the at least one requirement in the search requirement of the user through the pre-trained classification model based on the search sentence and the user information, and acquires the above knowledge in the pre-stored knowledge entity information set. At least one attribute information of the entity corresponding to the above requirements, and combining the obtained attribute information into a search result to join the search result page, displaying the content required by the user, and enriching the display content of the search result.
  • FIG. 4 illustrates a flow 400 of another embodiment of an information search method in accordance with the present application.
  • the information search method of this embodiment includes the following steps:
  • Step 401 Receive a search request.
  • the above search request includes a search sentence input by a user.
  • step 401 may refer to the related description of step 201 in the corresponding embodiment of FIG. 2, and details are not described herein again.
  • Step 402 Obtain an initial knowledge entity and an initial requirement that match the foregoing search statement by using a multi-pattern matching algorithm and according to a correspondence between the predetermined matching result and the initial knowledge entity and the initial requirement.
  • the multi-pattern matching algorithm may be an algorithm that performs matching by using a regular expression or by a suffix tree or the like.
  • the electronic device may acquire a pattern string (expression) matching the search sentence by using a multi-pattern matching algorithm as a matching result, and obtain an initial matching with the above-mentioned search sentence according to a correspondence between the predetermined matching result and the initial knowledge entity and the initial requirement.
  • Knowledge entities and initial needs.
  • the correspondence between the predetermined matching result and the initial knowledge entity and the initial requirement may be preset by the manual according to the domain knowledge, or may be obtained by the machine learning method.
  • the matching regular expression can be "Beijing* (how? Fun?)”.
  • the initial knowledge entity corresponding to the expression is “Beijing Tourism” and the initial requirements are “Introduction” and “Rommes”
  • the initial knowledge entity matching the search statement “Great Wall of Beijing?” be “ Beijing Tourism”
  • the initial demand is “introduction” and “raising”.
  • Step 403 Acquire the user information of the user, and obtain a knowledge entity in the search requirement of the user by using the pre-trained knowledge entity classification model based on the search sentence, the user information, the initial knowledge entity, and the initial requirement.
  • the electronic device may input the search sentence, the user information, the initial knowledge entity, and the initial requirement into the pre-trained knowledge entity classification model, and obtain a probability corresponding to each knowledge entity by using a classification algorithm.
  • the corresponding knowledge entity with the highest probability is used as the knowledge entity in the user's search requirements.
  • the knowledge entity classification model of the embodiment may be through a large number of entities
  • the labeled training data is obtained through learning and training.
  • the training data may include the following information: a search term, user information, an initial knowledge entity, an initial requirement, a knowledge entity, and an annotation, wherein the annotation is used to indicate whether the search term is related to the knowledge entity of the training data.
  • Step 404 Obtain the at least one requirement by using a pre-trained demand classification model based on the search statement, the user information, and the knowledge entity.
  • the specific processing of the step 404 may refer to the related description of the related alternative implementation manner in the corresponding embodiment of FIG. 2, and details are not described herein again.
  • Step 405 Acquire at least one attribute information of the foregoing knowledge entity in the pre-stored knowledge entity information set.
  • the at least one attribute information is in one-to-one correspondence with the at least one requirement.
  • step 405 can refer to the related description of step 203 in the corresponding embodiment of FIG. 2, and details are not described herein again.
  • Step 406 Combine the acquired attribute information into a search result and join the search result page.
  • step 406 may refer to the related description of step 204 in the corresponding embodiment of FIG. 2, and details are not described herein again.
  • step 404 may include: obtaining, by using the pre-trained demand classification model, based on the search query, the user information, the knowledge entity, the initial knowledge entity, and the initial requirement. At least one requirement.
  • the electronic device may input the search term, the user information, the knowledge entity in the search requirement acquired in step 403, the initial knowledge entity, and the initial requirement into the pre-trained demand classification model, and obtain the probability corresponding to each requirement. The corresponding probability is selected in order from the largest to the smallest, as a demand of at least one of the above search requirements.
  • the above demand classification model may be obtained by learning and training a large number of training data with demand labels.
  • the training data may include the following information: a search term, a user information, a knowledge entity, an initial knowledge entity, an initial requirement, a requirement, and an annotation, wherein the annotation is used to indicate whether the search term is related to the knowledge entity and the requirement of the training data.
  • At least one requirement associated with the knowledge entity in the obtained user's retrieval requirements is made more accurate and scientific, so that the user needs can be more accurately displayed.
  • the information retrieval method of the present embodiment may further include: after receiving the search request, obtaining the entity words in the search sentence by using a named entity recognition algorithm based on the search statement.
  • the named entity identification algorithm may be a CRF (Conditional Random Field) algorithm.
  • the step 403 may include: obtaining, by using the pre-trained knowledge entity classification model, the knowledge entity based on the search query, the user information, the initial knowledge entity, the initial requirement, the entity word, and the requirement word.
  • the knowledge entity classification model of the implementation manner may be obtained by training training on a large number of training data with entity annotation.
  • the foregoing training data of the implementation manner may include the following information: a search term, user information, an initial knowledge entity, an initial requirement, an entity word, a requirement word, a knowledge entity, and an annotation, wherein the annotation is used to indicate whether the search term is related to the training.
  • the knowledge entity of the data is related.
  • the implementation method adds the entity words and the requirement words in the above-mentioned search sentences obtained by the named entity recognition algorithm to the reference factors of the knowledge entity classification, so that the obtained knowledge entities are more scientific, so as to more accurately display the content that the user needs.
  • step 404 may include: based on the foregoing search statement, the user information, the knowledge entity, the initial knowledge entity, the initial requirement, and the foregoing entity.
  • the word and the above demand word are obtained by the pre-trained demand classification model to obtain at least one of the above requirements.
  • the electronic device may input the search statement, the user information, the knowledge entity in the search requirement acquired in step 403, the initial knowledge entity, the initial requirement, the entity word, and the requirement word into a pre-trained demand classification model.
  • the probability corresponding to each requirement is obtained, and a predetermined number of requirements are sequentially selected according to the corresponding probability from the largest to the smallest, as at least one of the above-mentioned search requirements.
  • the above demand classification model may be obtained by learning and training a large number of training data with demand labels.
  • the training data may include the following information: a search term, a user information, a knowledge entity, an initial knowledge entity, an initial requirement, an entity word, a requirement word, a requirement, and an annotation, wherein the annotation is used to indicate whether the search term is related to the training data.
  • Knowledge entities are related to needs.
  • the implementation method adds the entity word and the requirement word in the above search sentence obtained by the named entity recognition algorithm to the reference factor of the demand classification, so that at least one requirement associated with the knowledge entity in the obtained user's retrieval requirement is more accurate and scientific. In order to more accurately display the content that the user needs.
  • the flow 400 of the information search method in the present embodiment increases the steps of obtaining an initial knowledge entity and initial requirements matching the search sentence by the multi-pattern matching algorithm as compared with the embodiment corresponding to FIG. 2. And add the initial knowledge entity and initial requirements to the reference factors for the classification of knowledge entities.
  • the solution described in this embodiment can make the obtained knowledge entity more scientific, thereby more accurately displaying the content that the user needs.
  • the present application provides an embodiment of an information search apparatus, and the apparatus embodiment corresponds to the method embodiment shown in FIG. Applied to the server.
  • the above-described information search apparatus 500 of the present embodiment includes a search request receiving unit 501, a search request acquiring unit 502, an attribute information acquiring unit 503, and a page generating unit 504.
  • the search request receiving unit 501 is configured to receive a search request, where the search request includes a search sentence input by the user;
  • the search requirement obtaining unit 502 is configured to acquire the user information of the user, and perform pre-training based on the search statement and the user information.
  • the classification model obtains the search requirement of the user, wherein the search requirement includes: a knowledge entity, at least one requirement associated with the knowledge entity; and the attribute information acquisition unit 503 is configured to acquire the knowledge entity in the pre-stored knowledge entity information set.
  • At least one attribute information wherein the at least one attribute information is in one-to-one correspondence with the at least one requirement;
  • the page generating unit 504 is configured to merge the acquired attribute information into a search result and join the search result page.
  • the specific processing of the search request receiving unit 501, the search request obtaining unit 502, the attribute information acquiring unit 503, and the page generating unit 504 may refer to step 201, step 202, step 203, and steps in the corresponding embodiment of FIG. 2, respectively.
  • the related description of 204 will not be repeated here.
  • the search requirement obtaining unit 502 may include: a knowledge entity obtaining subunit 5021, configured to obtain the foregoing by using a pre-trained knowledge entity classification model based on the above-mentioned search sentence and the user information.
  • Knowledge entity The obtaining subunit 5022 is configured to obtain the at least one requirement by using a pre-trained demand classification model based on the search sentence, the user information, and the knowledge entity.
  • the information search apparatus 500 of the present embodiment may further include: a multi-pattern matching unit 505, configured to pass the multi-pattern matching algorithm after receiving the search request, and according to a predetermined match. The result is a correspondence with the initial knowledge entity and the initial requirement, and the initial knowledge entity and the initial requirement that match the above search sentence are obtained.
  • the knowledge entity obtaining sub-unit 5021 is further configured to obtain the knowledge entity by using a pre-trained knowledge entity classification model based on the foregoing search sentence, the user information, the initial knowledge entity, and the initial requirement.
  • the multi-mode matching unit 505 refer to the related description of step 402 in the corresponding embodiment of FIG. 4, and details are not described herein again.
  • the knowledge entity acquisition sub-unit 5021 of the implementation refer to the related description of step 403 in the corresponding embodiment of FIG. 4, and details are not described herein again.
  • the requirement acquisition subunit 5022 may be further configured to use the foregoing search statement, the user information, the knowledge entity, the initial knowledge entity, and the initial requirement. At least one of the above requirements is obtained by a pre-trained demand classification model.
  • a pre-trained demand classification model For the specific processing of the implementation manner and the technical effects of the proxy, refer to the related description of the corresponding implementation manner in the corresponding embodiment of FIG. 4, and details are not described herein again.
  • the information search apparatus 500 of the present embodiment may further include: a named entity identifying unit 506, configured to, after receiving the search request, a named entity recognition algorithm based on the search statement, Obtain the entity word and the requirement word in the above search sentence.
  • the knowledge entity obtaining subunit 5021 is further configured to obtain the foregoing knowledge by using the pre-trained knowledge entity classification model based on the foregoing search sentence, the user information, the initial knowledge entity, the initial requirement, the entity word, and the requirement word. entity.
  • a named entity identifying unit 506 configured to, after receiving the search request, a named entity recognition algorithm based on the search statement, Obtain the entity word and the requirement word in the above search sentence.
  • the knowledge entity obtaining subunit 5021 is further configured to obtain the foregoing knowledge by using the pre-trained knowledge entity classification model based on the foregoing search sentence, the user information, the initial knowledge entity, the initial requirement, the entity word, and the requirement word. entity.
  • the requirement The obtaining subunit 5022 may be further configured to obtain the at least one by using the pre-trained demand classification model based on the search query, the user information, the knowledge entity, the initial knowledge entity, the initial requirement, the entity word, and the requirement word. demand.
  • the pre-trained demand classification model based on the search query, the user information, the knowledge entity, the initial knowledge entity, the initial requirement, the entity word, and the requirement word. demand.
  • the attribute information acquired by the attribute information acquiring unit 503 includes at least one of the following: picture information and text information.
  • picture information and text information For the specific processing of the implementation and the technical effects of the proxy, refer to the related description of the optional implementation of the step 203 in the corresponding embodiment of FIG. 2, and details are not described herein again.
  • the information search device obtains the knowledge entity and the at least one requirement in the search request of the user through the pre-trained classification model based on the search sentence and the user information, and obtains the attribute information acquiring unit 503 through the attribute information acquiring unit 503. Obtaining at least one attribute information of the above-mentioned knowledge entity corresponding to the above-mentioned requirements in a pre-stored knowledge entity information set, and then merging the acquired attribute information into a search result by the page generating unit 504 to join the search result page, and displaying The content that the user needs, and enriches the display content of the search results.
  • FIG. 6 a block diagram of a computer system 600 suitable for use in implementing a server of an embodiment of the present application is shown.
  • computer system 600 includes a central processing unit (CPU) 601 that can be loaded into a program in random access memory (RAM) 603 according to a program stored in read only memory (ROM) 602 or from storage portion 606. And perform various appropriate actions and processes.
  • RAM random access memory
  • ROM read only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 600 are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also coupled to bus 604.
  • the following components are connected to the I/O interface 605: a storage portion 606 including a hard disk or the like; and a communication portion 607 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 607 performs communication processing via a network such as the Internet.
  • Driver 608 is also coupled to I/O interface 605 as needed.
  • a removable medium 609 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like, is mounted on the drive 608 as needed so that a computer program read therefrom is installed into the storage portion 606 as needed.
  • an embodiment of the present disclosure includes a computer program product comprising a computer program tangibly embodied on a machine readable medium, the computer program comprising program code for executing the method illustrated in the flowchart.
  • the computer program can be downloaded and installed from the network via communication portion 607, and/or installed from removable media 609.
  • the central processing unit (CPU) 601 the above-described functions defined in the method of the present application are performed.
  • each block of the flowchart or block diagrams can represent a module, a program segment, or a portion of code that includes one or more logic for implementing the specified.
  • Functional executable instructions can also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments of the present application may be implemented by software or by hardware.
  • the described unit may also be disposed in the processor, for example, as a processor including a search request receiving unit, a search requirement acquiring unit, an attribute information acquiring unit, and a page generating unit.
  • the names of these units do not constitute a limitation on the unit itself under certain circumstances.
  • the search request receiving unit may also be described as "a unit that receives a search request.”
  • the present application further provides a non-volatile computer storage medium, which may be a non-volatile computer storage medium included in the apparatus described in the foregoing embodiments; It may be a non-volatile computer storage medium that exists alone and is not assembled into the terminal.
  • the non-volatile computer storage medium stores one or more programs, when the one or more programs are executed by a device, causing the device to: receive a search request, the search request including a search sentence input by a user; Obtain The user information of the user, and based on the search statement and the user information, obtain a search requirement of the user by using a pre-trained classification model, where the search requirement includes: a knowledge entity, and the knowledge entity Acquiring at least one requirement of the at least one attribute; acquiring at least one attribute information of the knowledge entity in the pre-stored knowledge entity information set, wherein the at least one attribute information is in one-to-one correspondence with the at least one requirement; and the acquired attribute information is merged Add a search results page for a search result.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
PCT/CN2016/097291 2016-04-07 2016-08-30 信息搜索方法和装置 Ceased WO2017173773A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
KR1020187032155A KR102148691B1 (ko) 2016-04-07 2016-08-30 정보 검색 방법 및 장치
JP2018552167A JP6732938B2 (ja) 2016-04-07 2016-08-30 情報検索方法及び装置

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610214064.0A CN105677931B (zh) 2016-04-07 2016-04-07 信息搜索方法和装置
CN201610214064.0 2016-04-07

Publications (1)

Publication Number Publication Date
WO2017173773A1 true WO2017173773A1 (zh) 2017-10-12

Family

ID=56308607

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/097291 Ceased WO2017173773A1 (zh) 2016-04-07 2016-08-30 信息搜索方法和装置

Country Status (4)

Country Link
JP (1) JP6732938B2 (https=)
KR (1) KR102148691B1 (https=)
CN (1) CN105677931B (https=)
WO (1) WO2017173773A1 (https=)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110134860A (zh) * 2019-04-12 2019-08-16 阿里巴巴集团控股有限公司 用户画像生成方法、装置和设备
CN111915366A (zh) * 2020-07-20 2020-11-10 上海燕汐软件信息科技有限公司 一种用户画像构建方法、装置、计算机设备及存储介质
CN111949793A (zh) * 2020-08-13 2020-11-17 深圳市欢太科技有限公司 用户意图识别方法、装置及终端设备
CN112925883A (zh) * 2021-02-19 2021-06-08 北京百度网讯科技有限公司 搜索请求处理方法、装置、电子设备及可读存储介质
CN114139542A (zh) * 2021-11-25 2022-03-04 北京皮尔布莱尼软件有限公司 一种实体识别方法、装置及计算设备

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105677931B (zh) * 2016-04-07 2018-06-19 北京百度网讯科技有限公司 信息搜索方法和装置
CN108052613B (zh) * 2017-12-14 2021-12-31 北京百度网讯科技有限公司 用于生成页面的方法和装置
CN108256070B (zh) * 2018-01-17 2022-07-15 北京百度网讯科技有限公司 用于生成信息的方法和装置
CN110516226A (zh) * 2018-05-22 2019-11-29 广东神马搜索科技有限公司 搜索语句处理方法、装置及服务器
CN110633476B (zh) * 2019-09-27 2024-04-05 北京百度网讯科技有限公司 用于获取知识标注信息的方法及装置
CN111241400B (zh) * 2020-01-14 2023-04-25 北京字节跳动网络技术有限公司 一种信息搜索方法及装置
CN111708943B (zh) * 2020-06-12 2024-03-01 北京搜狗科技发展有限公司 一种搜索结果展示方法、装置和用于搜索结果展示的装置
CN113221572B (zh) * 2021-05-31 2024-05-07 抖音视界有限公司 一种信息处理方法、装置、设备及介质
CN115203549A (zh) * 2022-07-13 2022-10-18 上海喜马拉雅科技有限公司 一种搜索类目预测方法、装置、存储介质及电子设备

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102073729A (zh) * 2011-01-14 2011-05-25 百度在线网络技术（北京）有限公司 一种关系化知识共享平台及其实现方法
CN103425714A (zh) * 2012-05-25 2013-12-04 北京搜狗信息服务有限公司 一种搜索方法和系统
CN104199855A (zh) * 2014-08-13 2014-12-10 王和平 一种针对中医药学信息的检索系统和方法
CN104598556A (zh) * 2015-01-04 2015-05-06 百度在线网络技术（北京）有限公司 搜索方法及装置
CN104866498A (zh) * 2014-02-24 2015-08-26 华为技术有限公司 一种信息处理方法及装置
CN105677931A (zh) * 2016-04-07 2016-06-15 北京百度网讯科技有限公司 信息搜索方法和装置

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003228580A (ja) * 2002-02-04 2003-08-15 Celestar Lexico-Sciences Inc 文献知識管理装置、文献知識管理方法、プログラム、および、記録媒体
JP2003256466A (ja) * 2002-03-04 2003-09-12 Denso Corp 適応的情報検索システム
US7584177B2 (en) * 2005-06-29 2009-09-01 Google Inc. Determination of a desired repository
JP5013701B2 (ja) * 2005-10-26 2012-08-29 ヤフー株式会社 検索装置及び検索方法
CN101339551B (zh) * 2007-07-05 2013-01-30 日电（中国）有限公司 自然语言查询需求扩展设备及其方法
JP2009282903A (ja) * 2008-05-26 2009-12-03 Nippon Telegr & Teleph Corp <Ntt> 知識抽出・検索装置およびその方法
US9665643B2 (en) * 2011-12-30 2017-05-30 Microsoft Technology Licensing, Llc Knowledge-based entity detection and disambiguation
JP5250709B1 (ja) * 2012-03-12 2013-07-31 楽天株式会社 情報処理装置、情報処理方法、情報処理装置用プログラム、および、記録媒体
CN103853722B (zh) * 2012-11-29 2017-09-22 腾讯科技（深圳）有限公司 一种基于检索串的关键词扩展方法、装置和系统
CN103425741A (zh) * 2013-07-16 2013-12-04 北京中科汇联信息技术有限公司 一种信息展示方法和装置
KR101553848B1 (ko) * 2014-02-17 2015-09-17 네이버 주식회사 검색 결과를 제공하는 방법 및 상기 방법을 수행하는 시스템
CN103914566A (zh) * 2014-04-22 2014-07-09 百度在线网络技术（北京）有限公司 搜索结果的展现方法和装置
CN104462505A (zh) * 2014-12-19 2015-03-25 北京奇虎科技有限公司 搜索方法和装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102073729A (zh) * 2011-01-14 2011-05-25 百度在线网络技术（北京）有限公司 一种关系化知识共享平台及其实现方法
CN103425714A (zh) * 2012-05-25 2013-12-04 北京搜狗信息服务有限公司 一种搜索方法和系统
CN104866498A (zh) * 2014-02-24 2015-08-26 华为技术有限公司 一种信息处理方法及装置
CN104199855A (zh) * 2014-08-13 2014-12-10 王和平 一种针对中医药学信息的检索系统和方法
CN104598556A (zh) * 2015-01-04 2015-05-06 百度在线网络技术（北京）有限公司 搜索方法及装置
CN105677931A (zh) * 2016-04-07 2016-06-15 北京百度网讯科技有限公司 信息搜索方法和装置

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110134860A (zh) * 2019-04-12 2019-08-16 阿里巴巴集团控股有限公司 用户画像生成方法、装置和设备
CN110134860B (zh) * 2019-04-12 2023-04-07 创新先进技术有限公司 用户画像生成方法、装置和设备
CN111915366A (zh) * 2020-07-20 2020-11-10 上海燕汐软件信息科技有限公司 一种用户画像构建方法、装置、计算机设备及存储介质
CN111915366B (zh) * 2020-07-20 2024-01-12 上海燕汐软件信息科技有限公司 一种用户画像构建方法、装置、计算机设备及存储介质
CN111949793A (zh) * 2020-08-13 2020-11-17 深圳市欢太科技有限公司 用户意图识别方法、装置及终端设备
CN111949793B (zh) * 2020-08-13 2024-02-27 深圳市欢太科技有限公司 用户意图识别方法、装置及终端设备
CN112925883A (zh) * 2021-02-19 2021-06-08 北京百度网讯科技有限公司 搜索请求处理方法、装置、电子设备及可读存储介质
CN112925883B (zh) * 2021-02-19 2024-01-19 北京百度网讯科技有限公司 搜索请求处理方法、装置、电子设备及可读存储介质
CN114139542A (zh) * 2021-11-25 2022-03-04 北京皮尔布莱尼软件有限公司 一种实体识别方法、装置及计算设备

Also Published As

Publication number Publication date
CN105677931B (zh) 2018-06-19
JP6732938B2 (ja) 2020-07-29
CN105677931A (zh) 2016-06-15
JP2019511065A (ja) 2019-04-18
KR102148691B1 (ko) 2020-08-27
KR20180126589A (ko) 2018-11-27

Similar Documents

Publication Publication Date Title
WO2017173773A1 (zh) 信息搜索方法和装置
JP6745384B2 (ja) 情報をプッシュするための方法及び装置
US11151177B2 (en) Search method and apparatus based on artificial intelligence
CN107273503B (zh) 用于生成同语言平行文本的方法和装置
KR102354716B1 (ko) 딥 러닝 모델을 이용한 상황 의존 검색 기법
CN107256267B (zh) 查询方法和装置
US20180336193A1 (en) Artificial Intelligence Based Method and Apparatus for Generating Article
CN107241260B (zh) 基于人工智能的新闻推送的方法和装置
CN107346336B (zh) 基于人工智能的信息处理方法和装置
US20190251087A1 (en) Method and apparatus for providing aggregate result of question-and-answer information
WO2018149115A1 (zh) 用于提供搜索结果的方法和装置
CN110059172B (zh) 基于自然语言理解的推荐答案的方法和装置
CN107944025A (zh) 信息推送方法和装置
US10437894B2 (en) Method and system for app search engine leveraging user reviews
CN108153901A (zh) 基于知识图谱的信息推送方法和装置
CN109766418B (zh) 用于输出信息的方法和装置
JP2019511065A5 (https=)
CN106993030A (zh) 基于人工智能的信息推送方法和装置
CN108287927B (zh) 用于获取信息的方法及装置
CN107305578A (zh) 人机智能问答方法和装置
CN112926308B (zh) 匹配正文的方法、装置、设备、存储介质以及程序产品
US20210004406A1 (en) Method and apparatus for storing media files and for retrieving media files
CN108268450A (zh) 用于生成信息的方法和装置
US9965474B2 (en) Dynamic summary generator
CN117808043B (zh) 信息处理方法、模型的训练方法、装置、设备及介质

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2018552167

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 20187032155

Country of ref document: KR

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16897705

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 16897705

Country of ref document: EP

Kind code of ref document: A1