CN102096717A

CN102096717A - Search method and search engine

Info

Publication number: CN102096717A
Application number: CN 201110038433
Authority: CN
Inventors: 刘建柱
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Baidu Online Network Technology Beijing Co Ltd; Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2011-02-15
Filing date: 2011-02-15
Publication date: 2011-06-15
Anticipated expiration: 2031-02-15
Also published as: CN102096717B

Abstract

The invention provides a search method. The search method comprises the following steps of: S1, receiving a query command; S2, performing demand intention analysis on the query command based on a knowledge base, and defining the demand intention of the query command; S3, searching the query command carrying the demand intention in a database to obtain a search result; and S4, outputting the search result. Compared with the prior art, the search method has the advantages that: on the basis of the knowledge base, the query command input by a user is understood better; the intention of the query command is analyzed; the structure of the query command is analyzed to perform semantic content expansion on the query command so as to better guide a search engine to select quality resources to meet the search requirements on the user; therefore, the search efficiency of the user is improved and the network traffic is saved.

Description

Searching method and search engine

Technical field

The present invention relates to search engine technique, relate in particular to a kind of searching method and search engine that query statement is carried out demand analysis and parsing based on knowledge base.

Background technology

Growth at full speed along with internet information has been full of increasing redundant information on the network, and for the Internet user who searches own required information on network, faces these information that extend endlessly undoubtedly as looking for a needle in a haystack.The appearance of search engine has brought convenience for user's search need undoubtedly to a certain extent.Search engine is a kind of software systems of using on network, and it is collected on network and discovery information with certain strategy, and after information being handled and organized, for the user provides information search service on the internet.Usually, this software systems provide a web interface, allow the user submit search word in client to by browser software, return very soon then one may with the relevant information list of search content of user input.This tabulation can comprise up to ten thousand clauses and subclauses usually, and each clauses and subclauses is represented one piece of related web page that searches.

Since more than ten years in past, correspondingly, arise at the historic moment in numerous internet search engines and corresponding website, and the outstanding person in the middle of this comprises Baidu's search (www.baidu.com) of company of Baidu and Google's search (www.google.cn) of Google.

The query statement that existing search engine is imported the user is based on the understanding of query statement character mostly, for example, the user input query instruction is " Nokia mobile phone ", based on existing search engine can only be " Nokia " and " mobile phone " with this query statement participle, and in the web database index, retrieve by this word segmentation result, text is comprised the webpage Url input of " Nokia " and " mobile phone ", form Search Results, yet this search engine can not carry out the understanding on content and the semantic hierarchies to user's query statement, for example, the user input query instruction is " Nokia mobile phone ", and it can not be interpreted as this query statement that " Nokia " is a kind of brand in " mobile phone "; Certainly, more can not understand the demand intention of query statement, and the structure of query statement, can not carry out semantic content expansion etc. to query statement.Expression-form variation, demand for user's input are intended to diversified query statement, the existing demand that can not better meet the user based on the search engine of character, cause be used to search incomplete, need repeatedly import the Search Results that different query statements just may find to be needed, search efficiency is lower, the problem of waste Internet resources.

Summary of the invention

The object of the present invention is to provide a kind of improved searching method, it can be on the basis of knowledge base, better understand the query statement of user's input, the analysis and consult instruction scheme, resolve the structure of query statement, query statement is carried out the semantic content expansion.

The present invention also aims to provide a kind of improved search engine of realizing above-mentioned searching method.

One of for achieving the above object, first embodiment of the invention provides a kind of searching method, may further comprise the steps:

S1, reception query statement;

S2, based on knowledge base described query statement is carried out the analysis of demand intention, the demand intention of clear and definite described query statement;

S3, the described query statement that will have a demand intention are searched in database, obtain Search Results;

S4, export described Search Results.

As a further improvement on the present invention, described database is web page repository or is intended to corresponding vertical search database with described demand.

As a further improvement on the present invention, between described S2 step and S3 step, also comprise the semantic step that expands:

Based on described knowledge base described query statement being carried out semanteme expands.

As a further improvement on the present invention, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

S200, give each demand intention marking of each knowledge fragment in the knowledge base, make each knowledge fragment all have corresponding demand intention score by user's historical behavior storehouse;

S201, with described query statement and knowledge fragment match, at least one the knowledge fragment that obtains being complementary with described query statement;

The demand intention score of S202, the knowledge fragment that will be complementary with described query statement adds up, and obtains first mark;

S203, the subordinate relation of knowledge fragment in described knowledge base by being complementary with described query statement are added and subtracted described first mark, obtain knowledge base integrated demand score;

S204, judge that whether described knowledge base integrated demand score is greater than a setting threshold;

S205, if greater than described setting threshold, then with the demand intention of the highest demand type of described knowledge base integrated demand score as described query statement;

S206, if less than described setting threshold, judge that then described query statement does not have obvious demand intention.

S201, with described query statement and knowledge fragment and expression template coupling, at least one the knowledge fragment and the expression template that obtain being complementary with described query statement;

S204, described query statement is given a mark on the expression template aspect, obtain the expression template score;

S205, with the weighting sum of knowledge base integrated demand score and expression template score as query statement demand intensity score;

S206, judge that whether described query statement demand intensity score is greater than a setting threshold;

S207, if greater than described setting threshold, then with the demand intention of the highest demand type of query statement demand intensity score as described query statement;

S208, if less than described setting threshold, judge that then described query statement does not have obvious demand intention.

One of for achieving the above object, second embodiment of the invention provides a kind of searching method, may further comprise the steps:

S1, reception query statement;

S2, based on knowledge base described query statement is carried out the analysis of demand intention, the demand intention of clear and definite described query statement simultaneously, is carried out semanteme expansion based on described knowledge base to described query statement;

S3, will have demand intention and expand semantic query statement and in database, search for, obtain Search Results;

S4, export described Search Results.

One of for achieving the above object, third embodiment of the invention provides a kind of searching method, may further comprise the steps:

S1, reception query statement;

S2, based on knowledge base and expression template storehouse described query statement is carried out the demand intention and analyze the demand intention of clear and definite described query statement;

S4, export described Search Results.

As a further improvement on the present invention, the construction method in described expression template storehouse comprises following flow process:

S300, extraction comprise the query statement of knowledge fragment in user's historical behavior storehouse;

S301, described knowledge base fragment is replaced to general symbol(s), generate candidate's expression template;

The knowledge base number of fragments that described candidate's expression template that S302, statistics generate meets;

S303, judge that whether described quantity is greater than setting threshold;

S304, if greater than setting threshold, then with described candidate's expression template as expression template, and be stored in the database, generate the expression template storehouse;

S305, if less than setting threshold, then give up described candidate's expression template.

One of for achieving the above object, four embodiment of the invention provides a kind of searching method, may further comprise the steps:

S1, reception query statement;

S2, based on knowledge base and expression template storehouse described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement simultaneously, is carried out semanteme expansion based on described knowledge base to the query statement that receives;

S4, export described Search Results.

S303, judge that whether described quantity is greater than setting threshold;

Correspondingly, as realizing another purpose of foregoing invention, an embodiment of the present invention provides a kind of search engine, comprising:

The UI module is used to receive query statement, and described UI module also is used to receive the Search Results that search module returns, and exports after described Search Results is assemblied into results page;

Demand intention analysis module is used for based on knowledge base described query statement being carried out the demand intention and analyzes the demand intention of clear and definite described query statement;

Search module is used for the described query statement that has the demand intention is searched at database, obtains Search Results;

Knowledge base is used to store priori.

As a further improvement on the present invention, described search engine also comprises:

The web service module, be used for receiving the query statement that client is sent by procotol, and forward described query statement to described UI module, and described web service module also is used to receive the results page that described UI module is returned, and described results page is back to described client.

User's historical behavior storehouse is used to store user's historical search record.

As a further improvement on the present invention, described user's historical search record comprises: query statement, inquiry times, and weighting clicks.

Expression template is excavated module, is used for knowledge fragment and the instruction of the user's historical query in described user's historical behavior storehouse according to described knowledge base, excavates expression template, and described expression template is stored in the expression template storehouse;

The expression template storehouse is used to store by described expression template and excavates the expression template that module is excavated.

The textural classification module is used for based on described knowledge base described query statement being carried out semanteme and expands.

As a further improvement on the present invention, described web page repository is used to store the index information of web data and this web data;

Described vertical search database is used to store the index information of particular category data and these particular category data.

Compared with prior art, the invention has the beneficial effects as follows: on the basis of knowledge base, better understand the query statement of user's input, analysis and consult instruction to scheme, resolve the structure of query statement, query statement is carried out semantic content expand, thereby better the guidance search engine selects the resource of high-quality to satisfy user's search need, make user search efficient improve, save network traffics.

Description of drawings

Fig. 1 is that search engine of the present invention and client realize interactive fundamental diagram;

Fig. 2 is the module map of search engine first embodiment of the present invention;

Fig. 3 is the module map of search engine second embodiment of the present invention;

Fig. 4 is the module map of search engine the 3rd embodiment of the present invention;

Fig. 5 is the module map of search engine the 4th embodiment of the present invention;

Fig. 6 is the synoptic diagram of knowledge base framework of the present invention;

Fig. 7 is the process flow diagram of searching method first embodiment of the present invention;

Fig. 8 is the process flow diagram of searching method second embodiment of the present invention;

Fig. 9 is the process flow diagram of searching method the 3rd embodiment of the present invention;

Figure 10 is the process flow diagram of searching method the 4th embodiment of the present invention;

Figure 11 is the process flow diagram that the present invention " carries out the demand intention based on knowledge base to described query statement and analyze, the demand intention of clear and definite described query statement " the step 1 embodiment;

Figure 12 is the process flow diagram that the present invention " carries out the demand intention based on knowledge base to described query statement and analyze, the demand intention of clear and definite described query statement " another embodiment of step;

Figure 13 is the process flow diagram of the construction method in expression template of the present invention storehouse;

Figure 14 is the synoptic diagram of the online interface of the present invention one embodiment;

Figure 15 is the new page synoptic diagram in " jumping to new page when the user clicks vertical search result ".

Embodiment

Describe the present invention below with reference to each embodiment shown in the drawings.But these embodiments do not limit the present invention, and the conversion on the structure that those of ordinary skill in the art makes easily according to these embodiments, method or the function all is included in protection scope of the present invention.

Search engine of the present invention 10 shown in Figure 1 is realized interactive fundamental diagram with client 20.In the present embodiment, this client 20 comprises a browser 201, the client can open the webpage of the online displaying of search engine by this browser 201, and input inquiry instruction in the dialog box in webpage, general, the query statement of this input is a text message, and certainly, this query statement can also be pictorial information, video information or the like.Described search engine 10 receives the client by network and inputs to query statement in the described browser, and after this query statement searched for, Search Results is back to this browser 201 by the online displayed web page of search engine.Wherein, this search engine 10 can comprise one or more server, this client 20 can comprise one or more subscriber terminal equipments, as personal computer, notebook computer, wireless telephone, personal digital assistant (PDA) or other department of computer science communication system of unifying.

These servers and terminal device all comprise some basic modules on framework, as bus, disposal system, storage system, one or more input/output and communication interface etc.Bus can comprise one or more leads, is used for realizing each communication between components of server or terminal device.Disposal system comprises that all types of being used for executed instruction, the processor or the microprocessor of treatment progress or thread.Storage system can comprise the random access storage device dynamic storagies such as (RAM) of storing multidate information and the ROM (read-only memory) static memories such as (ROM) of storing static information, and the mass storage that comprises magnetic or optical record medium and respective drive.Input system arrives server or terminal device for user's input information, as keyboard, mouse, writing pencil, sound recognition system or bioassay system etc.Output system comprises and is used for display, printer, loudspeaker of output information etc.Communication interface is used for making server or terminal device and other system or system to communicate.Can be connected in the network by wired connection, wireless connections or light between the communication interface, make search engine 10,20 of clients realize mutual communication by network.Network can comprise the combination etc. of internet, the Internet or above-mentioned these networks of Local Area Network, wide area network (WAN), telephone network such as public switch telephone network (PSTN), enterprises.

All include on server and the terminal device be used for management of system resource, control the operating system software of other program run, and the application software that is used for realizing certain functional modules.As shown in Figure 2, in first embodiment of the invention, described search engine has comprised web service module 101, UI module 102 with web service module 101 interactive communications, the demand intention analysis module 103 of communicating by letter with described UI module 102, the structure analysis module 104 of communicating by letter with described demand intention analysis module 103, the search module 105 of communicating by letter with described structure analysis module 104, and with described demand intention analysis module 103, the knowledge base 106 of described structure analysis module 104 interactive communications, user's historical behavior storehouse 107 of communicating by letter with described knowledge base 106, with described knowledge base 106, the expression template of user's historical behavior storehouse 107 communications is excavated module 108, excavate the expression template storehouse 109 that module 108 is communicated by letter with described demand intention analysis module 103 with described expression template, and the web page repository 110 of communicating by letter with described search module 105.What deserves to be mentioned is that these modules can store and run in the same server, also can store and operate in the multiple servers.

Described web service module 101 is used for receiving the query statement that transmits from client 20 by procotol, and forward this query statement to UI module 102, in addition, this web service module 101 also is used to receive the results page that described UI module 102 is returned, and described results page is back to client 20.

Described UI module 102 is used to receive the query statement that described web service module 101 transmits, and this query statement is sent to described query statement analysis module 103; In addition, described UI module 102 also is used to receive the Search Results that described search module 104 returns, and after described Search Results is assemblied into results page, returns described results page to described web service module 101.

Described demand intention analysis module 103 is used to call described knowledge base 106, user's historical behavior storehouse 107, and described expression template storehouse 108, analyzes the demand intention of clear and definite described query statement the query statement that receives is carried out the demand intention.In the present invention, described intention analysis module 103 is at first given each demand intention marking of each knowledge fragment in the described knowledge base 106 by described user's historical behavior storehouse 107, concrete: the user is when certain class demand of inquiry, can click the result who satisfies his demand accordingly, seek out the relevant information of AUTO QUOTED PRICE as the user, after search engine input inquiry instruction " bright ease ", the Url of the car website that meeting click search engine returns, as " Netease garage ", the Url " Netease garage " that query statement " bright ease " fragment of user's input this moment and user click implies the reflection user the demand of looking for the car relevant information, based on this point, the present invention is when calculating the demand intention of each knowledge fragment, click total Url that number/certain knowledge fragment is clicked of certain class Url according to certain knowledge fragment, determine the score of this knowledge fragment demand intention, as as described in learn in user's historical behavior storehouse 107, query statement is " a bright ease ", total Url number of its click is 10, wherein, commodity class Url is 5, news category Url is 3, picture category Url is 2, the demand that then can calculate the commodity class of this query statement is intended to 0.5, the demand of news category is intended to 0.3, and the demand of picture category is intended to 0.2; Secondly, behind a query statement that receives user's input, need knowledge coupling, obtain being present in the described query statement knowledge fragment in the described knowledge base 106 through knowledge base 106, and the knowledge base integrated demand intensity of the described query statement of COMPREHENSIVE CALCULATING.For example the user imports " the bright ease quotation of Shanghai Volkswagen ", then can obtain the knowledge fragment of " Shanghai Volkswagen " " bright ease " by knowledge base.After obtaining preliminary information, at first knowledge fragment " Shanghai Volkswagen " and " bright ease " demand intention score are separately added up, obtain first mark, secondly, by the relation of knowledge fragment " Shanghai Volkswagen ", add and subtract described first mark again with " bright ease ", obtain knowledge base integrated demand score, in best mode for carrying out the invention, if the pass of knowledge fragment is to belong to relation together, bonus point then; If the knowledge fragment is the non-relation that belongs to together, then subtract branch; If this knowledge base integrated demand score is greater than preset threshold, then with the demand intention of the highest demand type of knowledge base integrated demand score as query statement, and according to described demand intention adding corresponding tag information, for example " commodity ", " news ", " picture " etc. in described query statement.What deserves to be mentioned is: in preferred forms of the present invention, except calculation knowledge storehouse integrated demand gets exceptionally, when analyze demands is intended to, also will consider the marking on the expression template aspect: behind a query statement that receives user's input, need expression template coupling through expression template storehouse 108, obtain being present in the described query statement expression template fragment in the described expression template storehouse 108, for example user's input " the bright ease quotation of Shanghai Volkswagen " then identifies " XX quotation " template that exists in the query statement by user template.In according to said method acquire knowledge storehouse integrated demand score, query statement meets the user's request template again, also give a mark on the expression template aspect to described query statement in expression template storehouse 108, obtain the expression template score, then the demand intensity of whole query statement must be divided into the weighting sum of knowledge base integrated demand score and expression template score, if this weighting sum is greater than preset threshold, then the demand type of the maximum of weighting sum is as the demand intention of query statement, and according to described demand intention adding corresponding tag information, for example " commodity " in described query statement, " news ", " picture " etc.

Described textural classification module 104 is used in conjunction with described knowledge base 106, to carrying out being sent to described search module 105 after the intelligent conversion through the query statement behind the described demand intention analysis module 103, wherein, described intelligent conversion promptly is that semantic content expands, described semantic content expands the normalization that has comprised semantic content, and the expansion of semantic classes.Concrete, when described query statement has the relation of belonging to together (going up the knowledge fragment of the knowledge fragment+following bit attribute of bit attribute), for example, described query statement is " a mobile phone Nokia ", at this moment, described textural classification module 104 is when sending to described search module 105, the tag that promptly can add " can abandon " on this on the knowledge fragment of bit attribute at " mobile phone ", like this, when 105 pairs of described query statements of search module are searched for, can search for by " mobile phone Nokia ", also can search for, and can think that also the webpage that has " mobile phone Nokia " text message is the same with the webpage weights that only have " Nokia " text message by " Nokia "; In addition, for example: if described query statement is " Nokia ", then described textural classification module 104 is when sending to described search module 105, also can according to " Nokia " corresponding on it bit attribute expand, as expand to " mobile phone ", like this, at search module 105 to " Nokia " when searching for, can judge whether that needs expand to " mobile phone " and search for according to result's quantity, as search by " Nokia " quantity is less as a result the time, can expand to " mobile phone "; And for example: if described query statement is " mobile phone ", then described textural classification module 104 can be expanded " mobile phone " corresponding its with bit attribute, as expand to " computer ", such expansion can be used for the popularization of advertisement, can carry out advertisement promotion according to " mobile phone " this query statement as right side, can carry out advertisement promotion according to " computer " this query statement again at searched page; For another example, if described query statement is " Nokia ", then described textural classification module 104 is when sending to described search module 105, also can expand according to " Nokia " corresponding its following bit attribute, as expand to " N71 ", " N72 " etc., like this, at search module 105 when searching text message webpages such as having " N71 ", " N72 ", also can judge whether to export according to the weights of these webpages as Search Results.This weights are judged the weights judgement that can join in the existing search engine, do not repeat them here.Generally speaking, when described query statement is expanded, can expand the knowledge fragment of bit attribute on it, also can expand its knowledge fragment, also can expand its knowledge fragment of bit attribute down with bit attribute according to the strategy of search.

Described search module 105 is used to receive the query statement through after the intelligent conversion of described demand intention analysis module 103 or described textural classification module 104, and this query statement searched in web page repository 110, to obtain Search Results, simultaneously, described search module 105 also is used for described Search Results is back to described UI module 102.

Described knowledge base 106 is used to store priori.In best mode for carrying out the invention, described knowledge base mainly is stored as tree structure, to knowledge base tree of each class construction of knowledge base, Father's Day point identification bit attribute on it by this knowledge base tree, the right brotgher of node is represented its same bit attribute, the left side brotgher of node is represented its bit attribute down, and iteration like this is until leaf node.As shown in Figure 6, " masses " are its upper attribute; Bit attribute is " Shanghai Volkswagen " under it; Have " FAW-Volkswagen " with described " Shanghai Volkswagen " coordination; In described " Shanghai Volkswagen " the next having " bright ease ", with have " way is seen " of described " bright ease " coordination ... the construction method of this knowledge base, those of ordinary skill in the art can join prior art and finish, and does not repeat them here.

User's historical behavior storehouse 107 is used to store user's historical search record.Preferably, it can comprise query statement, inquiry times, and information such as weighting clicks.

Expression template is excavated module 108 and is used for instructing according to the knowledge fragment and the user's historical query in described user's historical behavior storehouse 107 of described knowledge base 106, excavates expression template, and described expression template is stored in the described expression template storehouse 109.The user of same requirements, similarity can appear on expression way, described expression template is meant, the general user is when having certain query demand, the query statement of its input is why, for example, and as user during in inquiry automobile relevant information, expression way has: " Sagitar how ", " horse six power how " etc. wherein can extract the expression template of using always when " [automobile brand/model] how ", " [automobile brand/model] power how " etc. are expressed automobile demand.In one embodiment of the present invention, be specially: the query statement that at first comprises knowledge base 106 knowledge fragments in described user's historical behavior storehouse 107 extracts, as in the query statement of " horse six how ", " Skoda how ", " Sagitar how ", extract the knowledge fragment: " horse six ", " Skoda ", " Sagitar ", secondly the knowledge base fragment is replaced to " [automobile brand/model] " symbol, promptly generate candidate's expression template of " [automobile brand/model] how "; Once more, the knowledge base number of fragments that candidate's expression template that statistics generates meets, if this quantity is greater than preset threshold, then with described candidate's expression template as expression template, be stored in the described expression template storehouse 109; If this quantity less than preset threshold, is then given up described candidate's expression template.

Described expression template storehouse 109 is used to store by described expression template excavates the expression template that module 108 is excavated.

Described web page repository 110 is used to store the index information of web data and this web data.This database promptly is a general search engine database commonly used, does not repeat them here.

As shown in Figure 3, in second embodiment of the invention, described search engine has comprised web service module 101, UI module 102 with web service module 101 interactive communications, the demand intention analysis module 103 of communicating by letter with described UI module 102, the structure analysis module 104 of communicating by letter with described demand intention analysis module 103, the search module 105 of communicating by letter with described structure analysis module 104, and with described demand intention analysis module 103, the knowledge base 106 of described structure analysis module 104 interactive communications, user's historical behavior storehouse 107 of communicating by letter with described knowledge base 106, with described knowledge base 106, the expression template of user's historical behavior storehouse 107 communications is excavated module 108, excavate the expression template storehouse 109 that module 108 is communicated by letter with described demand intention analysis module 103 with described expression template, and the web page repository 110 of communicating by letter with described search module 105, the first vertical search data 111a, the second vertical search database 111b, ..., N vertical search database 111n.What deserves to be mentioned is that these modules can store and run in the same server, also can store and operate in the multiple servers.

Described demand intention analysis module 103 is used to call described knowledge base 106, user's historical behavior storehouse 107, and described expression template storehouse 108, analyzes the demand intention of clear and definite described query statement the query statement that receives is carried out the demand intention.In the present invention, described intention analysis module 103 is at first given each demand intention marking of each knowledge fragment in the described knowledge base 106 by described user's historical behavior storehouse 107, concrete: the user is when certain class demand of inquiry, can click the result who satisfies his demand accordingly, seek out the relevant information of AUTO QUOTED PRICE as the user, after search engine input inquiry instruction " bright ease ", the Url of the car website that meeting click search engine returns, as " Netease garage ", the Url " Netease garage " that query statement " bright ease " fragment of user's input this moment and user click implies the reflection user the demand of looking for the car relevant information, based on this point, the present invention is when calculating the demand intention of each knowledge fragment, click total Url that number/certain knowledge fragment is clicked of certain class Url according to certain knowledge fragment, determine the score of this knowledge fragment demand intention, as as described in learn in user's historical behavior storehouse 107, query statement is " a bright ease ", total Url number of its click is 10, wherein, commodity class Url is 5, news category Url is 3, picture category Url is 2, the demand that then can calculate the commodity class of this query statement is intended to 0.5, the demand of news category is intended to 0.3, and the demand of picture category is intended to 0.2; Secondly, behind a query statement that receives user's input, need knowledge coupling, obtain being present in the described query statement knowledge fragment in the described knowledge base 106 through knowledge base 106, and the knowledge base integrated demand intensity of the described query statement of COMPREHENSIVE CALCULATING.For example the user imports " the bright ease quotation of Shanghai Volkswagen ", then can obtain the knowledge fragment of " Shanghai Volkswagen " " bright ease " by knowledge base.After obtaining preliminary information, at first knowledge fragment " Shanghai Volkswagen " and " bright ease " demand intention score are separately added up, obtain first mark, secondly, by the relation of knowledge fragment " Shanghai Volkswagen ", add and subtract described first mark again with " bright ease ", obtain knowledge base integrated demand score, in best mode for carrying out the invention, if the pass of knowledge fragment is to belong to relation together, bonus point then; If the knowledge fragment is the non-relation that belongs to together, then subtract branch; If this knowledge base integrated demand score is greater than preset threshold, then with the demand intention of the highest demand type of knowledge base integrated demand score as query statement, and according to described demand intention adding corresponding tag information, for example " commodity ", " news ", " picture " etc. in described query statement.What deserves to be mentioned is: in preferred forms of the present invention, except calculation knowledge storehouse integrated demand gets exceptionally, when analyze demands is intended to, also will consider the marking on the expression template aspect: behind a query statement that receives user's input, need expression template coupling through expression template storehouse 108, obtain being present in the described query statement expression template fragment in the described expression template storehouse 108, for example user's input " the bright ease quotation of Shanghai Volkswagen " then identifies " XX quotation " template that exists in the query statement by user template.In according to said method acquire knowledge storehouse integrated demand score, query statement meets the user's request template again, also give a mark on the expression template aspect to described query statement in expression template storehouse 108, obtain the expression template score, then the demand intensity of whole query statement must be divided into the weighting sum of knowledge base integrated demand score and expression template score, if this weighting sum is greater than preset threshold, then the demand type of the maximum of weighting sum is as the demand intention of query statement.

Described search module 105 is used to receive the query statement through after the intelligent conversion of described demand intention analysis module 103 or described textural classification module 104, and with this query statement a plurality of vertical search databases (the first vertical search database 111a, the second vertical search database 111b ..., N vertical data storehouse 111n) one of them, and search in the described web page repository 1110, to obtain Search Results, simultaneously, described search module 105 also is used for described Search Results is back to described UI module 102.What deserves to be mentioned is: selecting certain vertical search database to carry out vertical search is to determine by the demand intention of query statement, for example: if the demand of query statement is intended to " commodity ", then search in commodity vertical search database; The demand of institute's query statement is intended to " picture ", then in picture vertical search database, search for, wherein, one or more result who in the vertical search database, searches, can be inserted among the result who in web page repository, searches, form whole Search Results.Described vertical search promptly is to search under certain specific classification, and searching method that it is concrete and system in the art more has prior art to disclose, and does not repeat them here.

The described first vertical search database 111a, the second vertical search database 111b ..., N vertical search database 111n is used to store the index information of particular category data and these particular category data, for example commodity data, commodity index; News data, news index; Image data, picture indices etc.

As shown in Figure 4, in third embodiment of the invention, described search engine has comprised web service module 101, UI module 102 with web service module 101 interactive communications, the demand intention analysis module 103 of communicating by letter with described UI module 102, the structure analysis module 104 of communicating by letter with described UI module 102, the search module 105 of communicating by letter with described structure analysis module 104, and with described demand intention analysis module 103, the knowledge base 106 of described structure analysis module 104 interactive communications, user's historical behavior storehouse 107 of communicating by letter with described knowledge base 106, with described knowledge base 106, the expression template of user's historical behavior storehouse 107 communications is excavated module 108, excavate the expression template storehouse 109 that module 108 is communicated by letter with described demand intention analysis module 103 with described expression template, and the web page repository 110 of communicating by letter with described search module 105.What deserves to be mentioned is that these modules can store and run in the same server, also can store and operate in the multiple servers.

Described textural classification module 104 is used in conjunction with described knowledge base 106, query statement to 102 inputs of UI module carries out being sent to described search module 105 after the intelligent conversion, wherein, described intelligent conversion promptly is that semantic content expands, described semantic content expands the normalization that has comprised semantic content, and the expansion of semantic classes.Concrete, when described query statement has the relation of belonging to together (going up the knowledge fragment of the knowledge fragment+following bit attribute of bit attribute), for example, described query statement is " a mobile phone Nokia ", at this moment, described textural classification module 104 is when sending to described search module 105, the tag that promptly can add " can abandon " on this on the knowledge fragment of bit attribute at " mobile phone ", like this, when 105 pairs of described query statements of search module are searched for, can search for by " mobile phone Nokia ", also can search for, and can think that also the webpage that has " mobile phone Nokia " text message is the same with the webpage weights that only have " Nokia " text message by " Nokia "; In addition, for example: if described query statement is " Nokia ", then described textural classification module 104 is when sending to described search module 105, also can according to " Nokia " corresponding on it bit attribute expand, as expand to " mobile phone ", like this, at search module 105 to " Nokia " when searching for, can judge whether that needs expand to " mobile phone " and search for according to result's quantity, as search by " Nokia " quantity is less as a result the time, can expand to " mobile phone "; And for example: if described query statement is " mobile phone ", then described textural classification module 104 can be expanded " mobile phone " corresponding its with bit attribute, as expand to " computer ", such expansion can be used for the popularization of advertisement, can carry out advertisement promotion according to " mobile phone " this query statement as right side, can carry out advertisement promotion according to " computer " this query statement again at searched page; For another example, if described query statement is " Nokia ", then described textural classification module 104 is when sending to described search module 105, also can expand according to " Nokia " corresponding its following bit attribute, as expand to " N71 ", " N72 " etc., like this, at search module 105 when searching text message webpages such as having " N71 ", " N72 ", also can judge whether to export according to the weights of these webpages as Search Results.This weights are judged the weights judgement that can join in the existing search engine, do not repeat them here.Generally speaking, when described query statement is expanded, can expand the knowledge fragment of bit attribute on it, also can expand its knowledge fragment, also can expand its knowledge fragment of bit attribute down with bit attribute according to the strategy of search.

As shown in Figure 5, in four embodiment of the invention, described search engine has comprised web service module 101, UI module 102 with web service module 101 interactive communications, the demand intention analysis module 103 of communicating by letter with described UI module 102, the structure analysis module 104 of communicating by letter with described UI module 102, the search module 105 of communicating by letter with described structure analysis module 104, and with described demand intention analysis module 103, the knowledge base 106 of described structure analysis module 104 interactive communications, user's historical behavior storehouse 107 of communicating by letter with described knowledge base 106, with described knowledge base 106, the expression template of user's historical behavior storehouse 107 communications is excavated module 108, excavate the expression template storehouse 109 that module 108 is communicated by letter with described demand intention analysis module 103 with described expression template, and the web page repository 110 of communicating by letter with described search module 105, the first vertical search data 111a, the second vertical search database 111b, ..., N vertical search database 111n.What deserves to be mentioned is that these modules can store and run in the same server, also can store and operate in the multiple servers.

Described textural classification module 104 is used in conjunction with described knowledge base 106, instruction is carried out being sent to described search module 105 after the intelligent conversion to UI module 102 input inquiries, wherein, described intelligent conversion promptly is that semantic content expands, described semantic content expands the normalization that has comprised semantic content, and the expansion of semantic classes.Concrete, when described query statement has the relation of belonging to together (going up the knowledge fragment of the knowledge fragment+following bit attribute of bit attribute), for example, described query statement is " a mobile phone Nokia ", at this moment, described textural classification module 104 is when sending to described search module 105, the tag that promptly can add " can abandon " on this on the knowledge fragment of bit attribute at " mobile phone ", like this, when 105 pairs of described query statements of search module are searched for, can search for by " mobile phone Nokia ", also can search for, and can think that also the webpage that has " mobile phone Nokia " text message is the same with the webpage weights that only have " Nokia " text message by " Nokia "; In addition, for example: if described query statement is " Nokia ", then described textural classification module 104 is when sending to described search module 105, also can according to " Nokia " corresponding on it bit attribute expand, as expand to " mobile phone ", like this, at search module 105 to " Nokia " when searching for, can judge whether that needs expand to " mobile phone " and search for according to result's quantity, as search by " Nokia " quantity is less as a result the time, can expand to " mobile phone "; And for example: if described query statement is " mobile phone ", then described textural classification module 104 can be expanded " mobile phone " corresponding its with bit attribute, as expand to " computer ", such expansion can be used for the popularization of advertisement, can carry out advertisement promotion according to " mobile phone " this query statement as right side, can carry out advertisement promotion according to " computer " this query statement again at searched page; For another example, if described query statement is " Nokia ", then described textural classification module 104 is when sending to described search module 105, also can expand according to " Nokia " corresponding its following bit attribute, as expand to " N71 ", " N72 " etc., like this, at search module 105 when searching text message webpages such as having " N71 ", " N72 ", also can judge whether to export according to the weights of these webpages as Search Results.This weights are judged the weights judgement that can join in the existing search engine, do not repeat them here.Generally speaking, when described query statement is expanded, can expand the knowledge fragment of bit attribute on it, also can expand its knowledge fragment, also can expand its knowledge fragment of bit attribute down with bit attribute according to the strategy of search.

As shown in Figure 7, the searching method of first embodiment of the invention may further comprise the steps:

S1, reception query statement; Preferably, this query statement be the user by the input of the browser on the client to web service module 101, this web service module 101 can forward this querying command to UI module 102 after obtaining described querying command;

S2, based on knowledge base described query statement is carried out the analysis of demand intention, the demand intention of clear and definite described query statement; Preferably, this step is finished by described demand intention analysis module 103;

S3, the described query statement that will have a demand intention are searched in database, obtain Search Results; Preferably, this step is finished by described search module 105;

S4, export described Search Results.Preferably, this step is finished in described UI module 102 and described web service module 101, Search Results is back to described UI module 102 from described search module 104, and after by described UI module 102 described Search Results being assemblied into results page, return described results page to described web service module 101, thereby be back to client browser by described web service module 101.

Wherein, the database in described S3 step promptly can be web page repository 110, or is intended to corresponding vertical search database with demand; Certainly, also can comprise web page repository 110 and be intended to corresponding vertical search database with described demand.

Between described S2 step and S3 step, also comprise the semantic step that expands:

Based on described knowledge base the query statement that receives being carried out semanteme expands; Preferably, this step is finished by structure analysis module 104.

As shown in Figure 8, the searching method of second embodiment of the invention may further comprise the steps:

S1 ', reception query statement; Preferably, this query statement be the user by the input of the browser on the client to web service module 101, this web service module 101 can forward this querying command to UI module 102 after obtaining described querying command;

S2 ', based on knowledge base described query statement is carried out the analysis of demand intention, the demand intention of clear and definite described query statement simultaneously, is carried out semanteme expansion based on described knowledge base to the query statement that receives; Preferably, this step is finished by described demand intention analysis module 103 and described structure analysis module 104;

S3 ', will have demand intention and expand semantic query statement and in database, search for, obtain Search Results; Preferably, this step is finished by described search module 105;

S4 ', export described Search Results.Preferably, this step is finished in described UI module 102 and described web service module 101, Search Results is back to described UI module 102 from described search module 104, and after by described UI module 102 described Search Results being assemblied into results page, return described results page to described web service module 101, thereby be back to client browser by described web service module 101.

Wherein, the database in described S3 ' step promptly can be web page repository 110, or is intended to corresponding vertical search database with demand; Certainly, also can comprise web page repository 110 and be intended to corresponding vertical search database with described demand.

As shown in Figure 9, the searching method of third embodiment of the invention may further comprise the steps:

S10, reception query statement; Preferably, this query statement be the user by the input of the browser on the client to web service module 101, this web service module 101 can forward this querying command to UI module 102 after obtaining described querying command;

S20, based on knowledge base and expression template storehouse described query statement is carried out the demand intention and analyze the demand intention of clear and definite described query statement; Preferably, this step is finished by described demand intention analysis module 103;

S30, the described query statement that will have a demand intention are searched in database, obtain Search Results; Preferably, this step is finished by described search module 105;

S40, export described Search Results.Preferably, this step is finished in described UI module 102 and described web service module 101, Search Results is back to described UI module 102 from described search module 104, and after by described UI module 102 described Search Results being assemblied into results page, return described results page to described web service module 101, thereby be back to client browser by described web service module 101.

Wherein, the database in described S30 step promptly can be web page repository 110, or is intended to corresponding vertical search database with demand; Certainly, also can comprise web page repository 110 and be intended to corresponding vertical search database with described demand.

Between described S20 step and S30 step, also comprise the semantic step that expands:

As shown in Figure 8, the searching method of four embodiment of the invention may further comprise the steps:

S10 ', reception query statement; Preferably, this query statement be the user by the input of the browser on the client to web service module 101, this web service module 101 can forward this querying command to UI module 102 after obtaining described querying command;

S20 ', based on knowledge base and expression template storehouse described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement simultaneously, is carried out semanteme expansion based on described knowledge base to the query statement that receives; Preferably, this step is finished by described demand intention analysis module 103 and described structure analysis module 104;

S30 ', will have demand intention and expand semantic query statement and in database, search for, obtain Search Results; Preferably, this step is finished by described search module 105;

S40 ', export described Search Results.Preferably, this step is finished in described UI module 102 and described web service module 101, Search Results is back to described UI module 102 from described search module 104, and after by described UI module 102 described Search Results being assemblied into results page, return described results page to described web service module 101, thereby be back to client browser by described web service module 101.

Wherein, the database in described S30 ' step promptly can be web page repository 110, or is intended to corresponding vertical search database with demand; Certainly, also can comprise web page repository 110 and be intended to corresponding vertical search database with described demand.

As shown in figure 11, in the searching method of first embodiment of the invention, second embodiment, the 3rd embodiment, the 4th embodiment, one embodiment of described " based on knowledge base described query statement being carried out the demand intention analyzes; the demand intention of clear and definite described query statement " step comprises following flow process:

S200, give each demand intention marking of each knowledge fragment in the knowledge base, make each knowledge fragment all have corresponding demand intention score by user's historical behavior storehouse; Concrete: the user is when certain class demand of inquiry, can click the result who satisfies his demand accordingly, seek out the relevant information of AUTO QUOTED PRICE as the user, after search engine input inquiry instruction " bright ease ", the Url of the car website that meeting click search engine returns, as " Netease garage ", the Url " Netease garage " that query statement " bright ease " fragment of user's input this moment and user click implies the reflection user the demand of looking for the car relevant information, based on this point, the present invention is when calculating the demand intention of each knowledge fragment, click total Url that number/certain knowledge fragment is clicked of certain class Url according to certain knowledge fragment, determine the score of this knowledge fragment demand intention, as as described in learn in user's historical behavior storehouse 107, query statement is " a bright ease ", total Url number of its click is 10, wherein, commodity class Url is 5, news category Url is 3, picture category Url is 2, the demand that then can calculate the commodity class of this query statement is intended to 0.5, the demand of news category is intended to 0.3, and the demand of picture category is intended to 0.2;

S201, after receiving a query statement of user input, with described query statement and knowledge fragment match, at least one the knowledge fragment that obtains being complementary with described query statement; For example the user imports " the bright ease quotation of Shanghai Volkswagen ", then can obtain the knowledge fragment of " Shanghai Volkswagen " " bright ease " by knowledge base;

The demand intention score of S202, the knowledge fragment that will be complementary with described query statement adds up, and obtains first mark; For example: knowledge fragment " Shanghai Volkswagen " and " bright ease " demand intention score separately adds up, and obtains first mark;

S203, the subordinate relation of knowledge fragment in described knowledge base by being complementary with described query statement are added and subtracted described first mark, obtain knowledge base integrated demand score; In best mode for carrying out the invention, if the pass of knowledge fragment is to belong to relation together, bonus point then; If the knowledge fragment is the non-relation that belongs to together, then subtract branch;

S206, if less than described setting threshold, judge that then described query statement does not have obvious demand intention, search for according to general search engine search mode, do not repeat them here.

As shown in figure 12, in the searching method of first embodiment of the invention, second embodiment, the 3rd embodiment, the 4th embodiment, another embodiment of described " based on knowledge base and expression template storehouse described query statement being carried out the analysis of demand intention; the demand intention of clear and definite described query statement " step comprises following flow process:

S200 ', give each demand intention marking of each knowledge fragment in the knowledge base, make each knowledge fragment all have corresponding demand intention score by user's historical behavior storehouse; Concrete: the user is when certain class demand of inquiry, can click the result who satisfies his demand accordingly, seek out the relevant information of AUTO QUOTED PRICE as the user, after search engine input inquiry instruction " bright ease ", the Url of the car website that meeting click search engine returns, as " Netease garage ", the Url " Netease garage " that query statement " bright ease " fragment of user's input this moment and user click implies the reflection user the demand of looking for the car relevant information, based on this point, the present invention is when calculating the demand intention of each knowledge fragment, click total Url that number/certain knowledge fragment is clicked of certain class Url according to certain knowledge fragment, determine the score of this knowledge fragment demand intention, as as described in learn in user's historical behavior storehouse 107, query statement is " a bright ease ", total Url number of its click is 10, wherein, commodity class Url is 5, news category Url is 3, picture category Url is 2, the demand that then can calculate the commodity class of this query statement is intended to 0.5, the demand of news category is intended to 0.3, and the demand of picture category is intended to 0.2;

S201 ', after receiving a query statement of user input, described query statement and knowledge fragment and the expression template that is stored in the expression template storehouse are mated at least one the knowledge fragment that obtains being complementary with described query statement and an expression template; For example the user imports " the bright ease quotation of Shanghai Volkswagen ", then can obtain the knowledge fragment of " Shanghai Volkswagen " " bright ease " by knowledge base; Obtain the expression template of " the XX quotation " that exist in the query statement by the expression template Cook;

The demand intention score of S202 ', the knowledge fragment that will be complementary with described query statement adds up, and obtains first mark; For example: knowledge fragment " Shanghai Volkswagen " and " bright ease " demand intention score separately adds up, and obtains first mark;

S203 ', the subordinate relation of knowledge fragment in described knowledge base by being complementary with described query statement are added and subtracted described first mark, obtain knowledge base integrated demand score; In best mode for carrying out the invention, if the pass of knowledge fragment is to belong to relation together, bonus point then; If the knowledge fragment is the non-relation that belongs to together, then subtract branch;

S204 ', described query statement is given a mark on the expression template aspect, obtain the expression template score;

S205 ', with the weighting sum of knowledge base integrated demand score and expression template score as query statement demand intensity score;

S206 ', judge that whether described query statement demand intensity score is greater than a setting threshold;

S207 ', if greater than described setting threshold, then with the demand intention of the highest demand type of query statement demand intensity score as described query statement;

S208 ', if less than described setting threshold, judge that then described query statement does not have tangible demand intention.

As shown in figure 13, in the searching method of third embodiment of the invention, the 4th embodiment, the construction method in described expression template storehouse comprises following flow process:

S300, extraction comprise the query statement of knowledge fragment in user's historical behavior storehouse; As in the query statement of " horse six how ", " Skoda how ", " Sagitar how ", extract the knowledge fragment: " horse six ", " Skoda ", " Sagitar ";

S301, described knowledge base fragment is replaced to general symbol(s), generate candidate's expression template; For example: " [automobile brand/model] " symbol, i.e. candidate's expression template of generation " [automobile brand/model] how ";

The knowledge base number of fragments that candidate's expression template that S302, statistics generate meets;

S303, judge that whether described quantity is greater than preset threshold;

S304, if greater than preset threshold, then with described candidate's expression template as expression template, and be stored in the database, generate the expression template storehouse;

By above-mentioned searching method and search engine, the online interface of one embodiment of the present invention as shown in figure 14, be used for opening the online interface of search engine of the present invention at browser, and input inquiry instruction " mobile phone Nokia " in dialog box, by above-mentioned searching method and search system, can judge the demand intention that this query statement has comprised the commodity classes, so in searching method of the present invention and search system, " mobile phone Nokia " this query statement can be searched in commodity vertical search database, simultaneously, insert among the result that this vertical search result searches for to the web page repository, A part as figure, when the user clicks described vertical search result, can jump in the new page, as shown in figure 15, comprised result for retrieval in this new page with commodity class demand intention, the B part can be found out from figure, does not comprise " mobile phone " this text message in this result for retrieval, promptly is the Search Results that obtains by semantic extension of the present invention.

In sum as can be known, the present invention is on the basis of knowledge base, better understand the query statement of user's input, analysis and consult instruction to scheme, resolve the structure of query statement, query statement is carried out semantic content expand, thereby better the guidance search engine selects the resource of high-quality to satisfy user's search need, make user search efficient improve, save network traffics.

Be to be understood that, though this instructions is described according to embodiment, but be not that each embodiment only comprises an independently technical scheme, this narrating mode of instructions only is for clarity sake, those skilled in the art should make instructions as a whole, technical scheme among each embodiment also can form other embodiments that it will be appreciated by those skilled in the art that through appropriate combination.

Above listed a series of detailed description only is specifying at feasibility embodiment of the present invention; they are not in order to restriction protection scope of the present invention, allly do not break away from equivalent embodiment or the change that skill spirit of the present invention done and all should be included within protection scope of the present invention.

Claims

1. a searching method is characterized in that, described searching method may further comprise the steps:

S1, reception query statement;

S4, export described Search Results.

2. searching method according to claim 1 is characterized in that, described database is web page repository or is intended to corresponding vertical search database with described demand.

3. searching method according to claim 1 is characterized in that, between described S2 step and S3 step, also comprises the semantic step that expands:

4. searching method according to claim 1 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

5. searching method according to claim 1 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

6. a searching method is characterized in that, described searching method may further comprise the steps:

S1, reception query statement;

S4, export described Search Results.

7. searching method according to claim 6 is characterized in that, described database is web page repository or is intended to corresponding vertical search database with described demand.

8. searching method according to claim 6 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

9. searching method according to claim 6 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

10. a searching method is characterized in that, described searching method may further comprise the steps:

S1, reception query statement;

S4, export described Search Results.

11. searching method according to claim 10 is characterized in that, described database is web page repository or is intended to corresponding vertical search database with described demand.

12. searching method according to claim 10 is characterized in that, between described S2 step and S3 step, also comprises the semantic step that expands:

13. searching method according to claim 10 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

14. searching method according to claim 10 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

15. searching method according to claim 10 is characterized in that, the construction method in described expression template storehouse comprises following flow process:

S303, judge that whether described quantity is greater than setting threshold;

16. a searching method is characterized in that, described searching method may further comprise the steps:

S1, reception query statement;

S4, export described Search Results.

17. searching method according to claim 16 is characterized in that, described database is web page repository or is intended to corresponding vertical search database with described demand.

18. searching method according to claim 16 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

19. searching method according to claim 16 is characterized in that, described " based on knowledge base described query statement is carried out the demand intention and analyze, the demand intention of clear and definite described query statement " specifically comprises following flow process:

20. searching method according to claim 16 is characterized in that, the construction method in described expression template storehouse comprises following flow process:

S303, judge that whether described quantity is greater than setting threshold;

21. a search engine is characterized in that, described search engine comprises:

Knowledge base is used to store priori.

22. search engine according to claim 21 is characterized in that, described search engine also comprises:

23. search engine according to claim 21 is characterized in that, described search engine also comprises:

24. search engine according to claim 23 is characterized in that, described user's historical search record comprises: query statement, inquiry times, and weighting clicks.

25., it is characterized in that described search engine also comprises according to claim 23 or 24 described search engines:

26. search engine according to claim 21 is characterized in that, described search engine also comprises:

27. search engine according to claim 21 is characterized in that, described database is web page repository or is intended to corresponding vertical search database with described demand.

28. search engine according to claim 27 is characterized in that,

Described web page repository is used to store the index information of web data and this web data;