CN101916294A - Method for realizing exact search by utilizing semantic analysis - Google Patents
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Abstract
The invention discloses a method for realizing exact search by utilizing semantic analysis. The method comprises the following steps of: (1) receiving a target information descriptor input by a user and performing segmentation operation on the target information descriptor; (2) judging whether the target information descriptor has complete semantics; (3) if so, performing subsequent search directly; and otherwise, providing vocabulary associated with the target information descriptor for the user; and (4) performing secondary input by the user so as to determine the semantics of the target information descriptor; and performing the subsequent search according to the semantics. By using the network search method, a very extract search result can be realized by only increasing very little user operation, and the satisfaction fundamentally can meet user requirement as much as possible.
Description
Technical field
The present invention relates to a kind of network search method, relate in particular to a kind of method of utilizing semantic analysis to realize precise search, belong to the web search technical field.
Background technology
The internet has become huge, a widely distributed information service center.Current, the data total amount in the internet is calculated with the hundreds of terabyte, and still with very fast speed increment.In order to help the user to obtain required information fast from the data ocean that this extends endlessly, search engine is being brought into play irreplaceable effect.
Search engine (search engine) is meant according to certain strategy, the specific computer program of utilization collects the information on the internet, after information being organized and is handled, and the information after will handling is shown to the user, thereby the information service system of retrieval service is provided for the user.Existing search engine is to enter the self database system according to the keyword that the user imports to retrieve, and the result that will retrieve feeds back to the user.In this process, maximum problem is that the user does not know import which type of keyword, could accurately express the information that oneself needs search.And the search service supplier need carry out analysis and judgement according to the information of user's input, and provides search information according to judged result.Therefore, often give an irrelevant answer between search service supplier's judgement and user's the demand.
As shown in Figure 1, when the search service supplier searches for according to the information of user's input, analyze according to the content of its input often, promptly carry out participle, after input information is divided into so-called " first vocabulary ", retrieve computing on a large scale according to these " first vocabulary ".Get over for a long time when the information of input, the retrieval computing of carrying out is also many more, so the computing power expense of search engine is bigger.For example when the user once imported two vocabulary, search engine will carry out the once matrix operation of ten million order of magnitude in theory.Therefore, present search engine drops into increasing on hardware, and the search effect does not obtain tangible improvement.On the other hand, the set of " first vocabulary ", promptly usually said " vocabulary " is also among increasing fast.At present, the vocabulary of maximum-norm has reached ten million order of magnitude." the first lexicon " of having gathered all " first vocabulary " in the human information space hereto, maximum problem is that the vocabulary content increases fast and irresistible gesture is arranged, so this " first vocabulary " has been difficult to play the effect as " metadata " of information space.If this lexicon is defined as some fixed ranges rigidly, can not reflect the fast-developing social reality that changes again, can not accurately satisfy user's needs fully.
Along with the continuous development of web search technology, the notion of intelligent search has appearred.So-called intelligent retrieval is to utilize dictionary for word segmentation, thesaurus, the dictionary of homonyms to improve retrieval effectiveness, further also can nonproductive poll on knowledge aspect or notion aspect, form knowledge hierarchy or conceptual network by subject dictionary, last the next dictionary, related peer dictionary retrieval process, give the prompting of user's intelligence knowledge, finally help the user to obtain best retrieval effectiveness.For example inquiry " computing machine ", the information relevant with " computer " also can be retrieved out; Can also further dwindle query context to " microcomputer ", " server " or enlarge inquiry to categories such as " infotech " or inquiry relevant " electronic technology ", " software ", " computer utilitys ".In addition, existing some search engine also provides so-called " association " function, promptly carries out statistical study according to former user's selection result, and predicts according to these analysis results, provides most probable result to come to select for the user.But in fact this can not solve the accuracy problem of web search, because for a large amount of crowds, has certain statistical law, and for certain of some particular users was once searched for, statistical law did not have too many meaning.
In number of patent application is 200910192409.7 Chinese invention patent application, a kind of intelligent retrieval system based on semantic analysis has been proposed, it comprises: load module is used for input characters or instruction; The functional object database is used for the memory function object; The semantic analysis search module is used for the literal of described load module input is carried out semantic analysis, and searches out the functional object relevant with this semanteme from the functional object database; Display device is used to the functional object that shows that the semantic analysis search module searches out; Select calling module, the functional object select target functional object that the instruction that is used for importing according to described input media shows from display device, and call this target function objects.This patented claim also provides a kind of intelligent search method based on semantic analysis simultaneously, can come predictive user operation intention according to the semanteme of input characters, search out the functional object that is associated with this semanteme and offer the user alternative, make things convenient for the user to find target function objects quickly and accurately.Thereby no longer need the user to remember menus at different levels exactly, also need not operate multilevel menu and search destination object.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of method of utilizing semantic analysis to realize precise search.This method is at first carried out semantic analysis to the content of user's input and is retrieved with related vocabulary, and select by the user, thereby further the target of definite network search makes search engine from database the information of wanting most in the user mind be offered the user exactly.
For realizing above-mentioned goal of the invention, the present invention adopts following technical scheme:
A kind of method of utilizing semantic analysis to realize precise search is characterized in that comprising following step:
(1) accepts the target information descriptor that the user imports, described target information descriptor is carried out the participle operation;
(2) judge whether described target information descriptor has complete semanteme;
(3) if then directly carry out follow-up retrieval; If not, then provide the vocabulary that is associated with described target information descriptor to the user;
(4) user carries out the secondary input, thereby determines the semanteme of described target information descriptor, carries out follow-up retrieval according to this semanteme.
Wherein in described step (1), described participle operation adopts maximization to divide word algorithm.
In the described step (2), in described target information descriptor, have " body " and " behavior ", and " body " thinks that described target information descriptor has complete semanteme when related with " behavior " formation.
In the described step (2),, then at first determine " body " in the described target information descriptor if described target information descriptor does not have complete semanteme.In described step (4), further determine " behavior " of described target information descriptor correspondence by user's secondary input then.
In the described step (3), deposit the vocabulary that is associated with described target information descriptor by first vocabulary linked database.
In described first vocabulary linked database, for certain first vocabulary S, use S{ci, dj} stores its related vocabulary, and ci is classified as ground floor, and dj classifies as the second layer.
In the described step (4), the user with vocabulary that described target information descriptor is associated in the mode selected carry out the secondary input.
Network search method provided by the present invention is decomposed into optional search one first step input of two steps vocabulary with an existing step search, according to system suggestion carry out second step input (or selection) again after, searching database provides the result.Compared with prior art, the present networks searching method only just can be realized very accurate search results by the user's operation that increases seldom, can cover user's request as much as possible substantially on satisfaction.
Description of drawings
The present invention is described in further detail below in conjunction with the drawings and specific embodiments.
Fig. 1 provides the schematic flow sheet of web search service for existing search engine;
Fig. 2 is the method flow diagram that utilizes semantic analysis to realize precise search provided by the present invention.
Embodiment
The general thought of present networks searching method is that the processing mode with existing search engine employed " input-participle-retrieval " is converted to the processing mode of " input-participle-semanteme judgement-retrieval ", promptly carrying out semanteme after the participle operation judges, whether the speech of judging input is to have to determine semantic information, if then directly carry out follow-up retrieval; If not, then provide the vocabulary that is associated with the speech of importing to the user.The user carries out secondary input (selecting) again in the vocabulary that is associated, so that accurately judge the true semanteme of user's input information, thereby obtain accurate web search results according to this semanteme.
As shown in Figure 2, the present networks searching method needs to carry out twice input operation in most cases showing as on the way of realization: after the user imports most important target information descriptor (input operation for the first time), carry out the retrieval of related vocabulary and offer the user, therefrom select (input operation for the second time) by the user, thereby clear and definite concrete ferret out makes search engine from database the information of wanting most in the user mind be offered the user exactly.Particularly, the present networks searching method increases " first vocabulary linked database " in search engine, encourage user's input in " first vocabulary linked database " can represent the speech of its ferret out, and this speech is the most important goal description of his information needed.After search engine is accepted the speech of user's input and is carried out maximum branch word segmentation, judge whether its input information has complete semanteme, if have then directly carry out follow-up search operation, if do not have by this search engine to the input speech in " first vocabulary linked database ", carry out association analysis, and according to the result for the user provides a multinomial selection, make the user describe its needed target information more accurately by further selection.This multinomial selection have contain all relate to first the input speech for information about, user's true purpose is just grasped by search engine very exactly like this, thus search engine can be fast and at low cost that the user is required result for retrieval offer the user.
Need to prove that the present networks searching method is not simply existing step search to be decomposed into two optional search of step, but both abandoned a common step search procedure, cast out multistep or indefinite step questioning again.This is based on following two results of study and is definite, that is:
One. for " first vocabulary ", a baseset of generally acknowledging relatively is 1,000,000 orders of magnitude, and when carrying out step search, " first vocabulary " of 1,000,000 orders of magnitude is not enough to express the development of up-to-date vocabulary again.If but what carry out is two step search, then can express the lexical space of 1,000,000 square number magnitude theoretically, promptly reach trillion orders of magnitude.This order of magnitude should be to express all possible metadata in the existing information space.And the order of magnitude of correlation database can carry out certain restriction or precision according to the requirement of practicality fully, thereby reduces the computing cost of search engine, reaches the purpose that reduces cost.
Two. realize that the present networks searching method need be by semantic analysis.Because semantic formation need comprise two parts, promptly so-called " body " and " behavior ".Have only these two parts all to have and form (comprising that two all is the multiple situations of determining such as " bodies ") when related, could form a significant semanteme.Therefore, two step search procedures judge at first whether user's input forms complete semanteme, be the target of the first step as not forming then " body " to be had made to order really, and the target of second step search is defined as " behavior ".Just can intactly constitute once effectively " semantic search " by the search of two steps like this, thereby provide the information that is fit to its needs for the user.
In the present networks searching method, " first vocabulary linked database " is not to represent with a kind of one-dimensional data of simple relational database, but adopts the related lexical matrix of a multidimensional to represent.Particularly, we carry out Unified coding for each first vocabulary, and the coding that will be correlated with is represented content as it.Concrete encoding scheme can adopt multiple existing technology to realize, for example XML mode, N3 mode and tlv triple mode etc. have not just been given unnecessary details in detail at this.After the coding of a certain vocabulary is determined, when carrying out effective association analysis between each vocabulary, produce the related information of certain vocabulary, promptly for certain first vocabulary S, use S{ci, dj} stores its related vocabulary, and ci classified as ground floor, dj classifies as the second layer.Ci herein, dj can select to set according to deviser's demand, for example ci classified by the intellectual discipline ownership, and dj classified by people's user demand etc.Represent that by this two layers of classified then certain vocabulary just produces related lexicon.As automobile one speech, the classification of the ground floor of its related vocabulary can be expressed as (vehicle, brand, manufacturer, dealer, maintenance, performance, picture) etc., and second layer classification just includes (car, truck, lorry) etc. as vehicle.Its complete representation is:
S{ vehicle car truck lorry
Brand ... ... ... ... ... ... ..
Manufacturer ... ... ... ... ... ... ..
Dealer ... ... ... ... ... ... ..
Maintenance ... ... ... ... ... ... ..
Performance ... ... ... ... ... ....
Picture ... ... ... ... ... ... .}
When the user uses the search engine that adopts the present networks searching method, if the speech of input is " automobile " for the first time, search engine is retrieving S=" automobile " afterwards, the related lexical matrix of " automobile " is detected, and provide (ci) represented text message, promptly provide related vocabulary to be " ci=vehicle; brand; manufacturer; dealer; maintenance; performance, picture "; and the user is when carrying out the secondary affirmation; if selection is vehicle one speech; then search engine will comprise { " car to all, truck, lorry " } and the text of vocabulary such as " vehicles " retrieve, thereby really the needed all the elements of user are retrieved out.
In another one embodiment of the present invention, certain user enters this search engine by the internet in the computer of its family (as the Xueyaun Road, Haidian District, Beijing City), its objective is in order to search for, so he has imported " industrial and commercial bank " speech in search box from the position of nearest industrial and commercial bank of his family.Can be immediately that relevant " industrial and commercial bank " these four words on the internet are the relevant text message of common search engine displays to the user according to the sort method of oneself.The user removes to select to comprise the webpage of own information needed from the options of tens of at least pages, clickthrough enters wherein then, searches out own required information from this webpage.And when using the search engine that adopts the present networks searching method, when user's input " industrial and commercial bank " four words, (maximization divides word algorithm as forward at first to adopt maximization to divide word algorithm, oppositely maximization divides word algorithm or probability maximization to divide word algorithm etc.) determine that this is a vocabulary, and according to whether having the principle of complete semanteme, judging this is not an input content with complete semanteme, and be a vocabulary, and be understood that the required information of user is that (understanding herein is based on the user and once effectively imports the information relevant with industrial and commercial bank, rather than the result that unconscious input produces, therefore can according to its input be a speech and simply judge).After understanding this application target, we just can list all lexical informations relevant with " industrial and commercial bank " relatively completely in first vocabulary linked database of search engine, and arrange according to the degree of association between these vocabulary and " industrial and commercial bank " this speech, for example " industrial and commercial bank site ", " industrial and commercial bank's business hours ", " Web bank of industrial and commercial bank " etc. also can be enumerated out by certain some vocabulary that relevance is the strongest.As " stock ", " brief introduction of organizations ", " latest news ", " address ", " service procedure ", " corporate culture " or the like.The user can select " address " during in the face of these related vocabulary.Then, search engine just can judge exactly that according to user's IP address in fact the information that the user will search for be exactly " address from the nearest industrial and commercial bank in this position, IP address ".Like this, search engine can be retrieved in the database of oneself exactly, and the webpage that will have " Xueyuan Road industrial and commercial bank address " directly is shown to the user, and the user can obtain own real required information fast.
More than the method for utilizing semantic analysis to realize precise search provided by the present invention is had been described in detail.To those skilled in the art, any conspicuous change of under the prerequisite that does not deviate from connotation of the present invention it being done all will constitute to infringement of patent right of the present invention, with corresponding legal responsibilities.
Claims (8)
1. method of utilizing semantic analysis to realize precise search is characterized in that comprising following step:
(1) accepts the target information descriptor that the user imports, described target information descriptor is carried out the participle operation;
(2) judge whether described target information descriptor has complete semanteme;
(3) if then directly carry out follow-up retrieval; If not, then provide the vocabulary that is associated with described target information descriptor to the user;
(4) user carries out the secondary input, thereby determines the semanteme of described target information descriptor, carries out follow-up retrieval according to this semanteme.
2. the method for utilizing semantic analysis to realize precise search as claimed in claim 1 is characterized in that:
In the described step (1), described participle operation adopts maximization to divide word algorithm.
3. the method for utilizing semantic analysis to realize precise search as claimed in claim 1 is characterized in that:
In the described step (2), in described target information descriptor, have " body " and " behavior ", and " body " thinks that described target information descriptor has complete semanteme when related with " behavior " formation.
4. the method for utilizing semantic analysis to realize precise search as claimed in claim 1 is characterized in that:
In the described step (2),, then at first determine " body " in the described target information descriptor if described target information descriptor does not have complete semanteme.
5. the method for utilizing semantic analysis to realize precise search as claimed in claim 4 is characterized in that:
In the described step (4), further determine " behavior " of described target information descriptor correspondence by user's secondary input.
6. the method for utilizing semantic analysis to realize precise search as claimed in claim 1 is characterized in that:
In the described step (3), deposit the vocabulary that is associated with described target information descriptor by first vocabulary linked database.
7. the method for utilizing semantic analysis to realize precise search as claimed in claim 6 is characterized in that:
In described first vocabulary linked database, for certain first vocabulary S, use S{ci, dj} stores its related vocabulary, and ci is classified as ground floor, and dj classifies as the second layer.
8. the method for utilizing semantic analysis to realize precise search as claimed in claim 1 is characterized in that:
In the described step (4), the user with vocabulary that described target information descriptor is associated in the mode selected carry out the secondary input.
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Cited By (13)
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| WO2012025040A1 (en) * | 2010-08-27 | 2012-03-01 | Huang Bin | Visualized search engine system and implementation method and application thereof |
| CN102779163A (en) * | 2012-06-18 | 2012-11-14 | 青岛禧泰房产数据技术有限公司 | Quantization searching method and quantization searching system |
| CN103064880A (en) * | 2012-11-23 | 2013-04-24 | 覃文浩 | Method, device and system based on searching information for providing users with website choice |
| CN103106282A (en) * | 2013-02-27 | 2013-05-15 | 王义东 | Method for search and display of webpage |
| CN103488752A (en) * | 2013-09-24 | 2014-01-01 | 沈阳美行科技有限公司 | POI (point of interest) searching method |
| CN104516902A (en) * | 2013-09-29 | 2015-04-15 | 北大方正集团有限公司 | Semantic information acquisition method and corresponding keyword extension method and search method |
| CN104679784A (en) * | 2013-12-03 | 2015-06-03 | 上海博科资讯股份有限公司 | O2B intelligent searching method and system |
| CN104881410A (en) * | 2014-02-27 | 2015-09-02 | 腾讯科技（深圳）有限公司 | Client platform information interaction method, device and system |
| CN105069037A (en) * | 2015-07-22 | 2015-11-18 | 无锡天脉聚源传媒科技有限公司 | Search content display method and apparatus |
| CN105260396A (en) * | 2015-09-16 | 2016-01-20 | 百度在线网络技术（北京）有限公司 | Word retrieval method and apparatus |
| CN109165292A (en) * | 2018-07-23 | 2019-01-08 | Oppo广东移动通信有限公司 | Data processing method, device and mobile terminal |
| CN109598000A (en) * | 2018-12-28 | 2019-04-09 | 百度在线网络技术（北京）有限公司 | Semantic relation recognition methods, device, computer equipment and storage medium |
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| WO2012025040A1 (en) * | 2010-08-27 | 2012-03-01 | Huang Bin | Visualized search engine system and implementation method and application thereof |
| CN102779163A (en) * | 2012-06-18 | 2012-11-14 | 青岛禧泰房产数据技术有限公司 | Quantization searching method and quantization searching system |
| CN103064880A (en) * | 2012-11-23 | 2013-04-24 | 覃文浩 | Method, device and system based on searching information for providing users with website choice |
| CN103064880B (en) * | 2012-11-23 | 2016-12-21 | 覃文浩 | A kind of methods, devices and systems providing a user with website selection based on search information |
| CN103106282B (en) * | 2013-02-27 | 2016-01-13 | 王义东 | A kind of method of Webpage search and displaying |
| CN103106282A (en) * | 2013-02-27 | 2013-05-15 | 王义东 | Method for search and display of webpage |
| CN103488752A (en) * | 2013-09-24 | 2014-01-01 | 沈阳美行科技有限公司 | POI (point of interest) searching method |
| CN103488752B (en) * | 2013-09-24 | 2017-08-25 | 沈阳美行科技有限公司 | A kind of search method of POI intelligent retrievals |
| CN104516902A (en) * | 2013-09-29 | 2015-04-15 | 北大方正集团有限公司 | Semantic information acquisition method and corresponding keyword extension method and search method |
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| CN104881410A (en) * | 2014-02-27 | 2015-09-02 | 腾讯科技（深圳）有限公司 | Client platform information interaction method, device and system |
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| CN105069037A (en) * | 2015-07-22 | 2015-11-18 | 无锡天脉聚源传媒科技有限公司 | Search content display method and apparatus |
| CN105260396A (en) * | 2015-09-16 | 2016-01-20 | 百度在线网络技术（北京）有限公司 | Word retrieval method and apparatus |
| CN105260396B (en) * | 2015-09-16 | 2019-09-03 | 百度在线网络技术（北京）有限公司 | Word retrieval method and device |
| CN109165292A (en) * | 2018-07-23 | 2019-01-08 | Oppo广东移动通信有限公司 | Data processing method, device and mobile terminal |
| CN109598000A (en) * | 2018-12-28 | 2019-04-09 | 百度在线网络技术（北京）有限公司 | Semantic relation recognition methods, device, computer equipment and storage medium |
| CN109598000B (en) * | 2018-12-28 | 2023-06-16 | 百度在线网络技术（北京）有限公司 | Semantic relation recognition method, semantic relation recognition device, computer equipment and storage medium |
| CN116737777A (en) * | 2023-08-16 | 2023-09-12 | 北京壁仞科技开发有限公司 | Method, computing device and computer readable storage medium for searching for target operators |
| CN116737777B (en) * | 2023-08-16 | 2023-11-28 | 北京壁仞科技开发有限公司 | Method, computing device and computer readable storage medium for searching for target operators |
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