CN102184204B - Auto fill method and system of intelligent Web form - Google Patents
Auto fill method and system of intelligent Web form Download PDFInfo
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Abstract
The invention discloses an auto fill method and a system of an intelligent Web form. The method comprises the steps of 1) extracting the tag names in the form and normalizing the tag names into the standardized names of a semantic library; 2) searching a resource library according to the standardized names and selecting a candidate value for filling the form; and 3) selecting the candidate value for filling the form. The system comprises the semantic library used for storing the mapping collection from tag names to the standardized names, the resource library used for storing the collection of 'tag name- candidate value', a normalizing module used for extracting the tag names in the form and normalizing the tag names to the standardized names of the semantic library, a retrieval module used for searching the resource library according to the standardized names and selecting the candidate value, and a form filling module used for filling the form according to the candidate value selected. The method and system can reduce the work load on information input in the form filling process for the user, relieve the user load and promote the user experience.
Description
Technical field
The present invention relates to a kind of form filling method and system, the content of particularly filling in does not in the past need Research on Automatic Filling and the system of again typing in new table.
Background technology
Along with popularizing of internet, E-Government, ecommerce and various office automation have obtained vigorous growth, and a lot of users need to carry out all kinds of issued transaction by filling in a large amount of lists, as making a report on plan, report business, data acquisition etc.
The content of list is comprised of the form fields that prompting is wanted the label of input content and needed following closely the user to input usually.Such as text box, check box, radio box, drop-down choice box etc.The user the data entry form single domain after, submit again server process to.And these lists often comprise the content of a lot of repetitions, and the user need to fill in unit information and the duplicate contents such as personal information of oneself repeatedly.For example, although the job applicant has had the resume of the form such as Word document, need to carry out when recruitment website is filled in personal information a large amount of copying, the manual operations such as stickup, fill name, sex, home address, tens of the contents such as education experience.The user need to repeat same work at different recruitment websites, waste time and energy, and makes mistakes easily.
There are some products and technology can reduce the work of making a report on middle repetition on the internet.For example, Autoformer can collect the essential informations such as user name, address, phone in registration, the simple list of login, and the user carries out automatic filling when run into similar list next time.When wherein new table was identical with the historical form label, the data that label of the same name is corresponding were remembered and are reused.When label not simultaneously, for example, " postcode " and " postcode " need to fill identical, this situation can't automatic filling.In the transacter of the reality of Internet-based, list is very universal, and data are complicated, and these simple automatic filling technology can't solve.
Summary of the invention
The purpose of this invention is to provide and a kind ofly can automatically identify semantic identical repeating part, solve and fill in duplicate contents problem intelligent web Auto Filling Forms method and system in the list.
Technical scheme of the present invention is:
A kind of intelligent web Auto Filling Forms method comprises semantic base and resources bank, and the method may further comprise the steps:
1) extracts tag name in the list, be normalized to the standard name of described semantic base;
2) search described resources bank according to described standard name, select described candidate value to fill in a form;
3) select described candidate value to fill in a form.
Further, the described tag name of described semantic base file probability is to the mapping probability of described standard name.
Further, the establishment step of described resources bank is:
21) extraction " tag name-candidate value " is right from reference documents or historical form padding data;
22) according to described semantic base that " tag name-candidate value " is right to being normalized to " standard name-candidate value ";
23) with described " standard name-candidate value " to being stored in described resources bank.
Further, the establishment step of described resources bank is:
201) extraction " tag name-candidate value " is right from reference documents or historical form padding data;
202) according to described semantic base that " tag name-candidate value " is right to being normalized to " standard name-candidate value-confidence level ";
203) with described " standard name-candidate value-confidence level " to being stored in described resources bank.
Further, described step 202) and step 203) between be provided with step 204), it is right less than " standard name-candidate value-confidence level " that filter threshold value to be used for the deletion confidence level.
A kind of intelligent web Auto Filling Forms system is characterized in that, comprising:
Semantic base is used for the tag name of storage to the mapping set of standard name;
Resources bank is used for storage " standard name-candidate value " right set;
The normalization module for the tag name of extracting list, is normalized to the standard name of described semantic base;
Retrieval module is used for searching resources bank according to described standard name, chooses candidate value;
The module of filling in a form is used for filling in a form according to the described candidate value of selecting.
Further, described semantic base is stored described tag name to the mapping probability of described standard name.
Further, described resources bank is stored " standard name-candidate value-confidence level " right set, is provided with the filtration threshold value of confidence level.
The invention has the beneficial effects as follows: by the mapping relations of semantic base, tag name in the form is carried out normalized, and from resources bank, choose candidate value and carry out automatic form filling, by this system and method, user's input is minimized, alleviate the burden that the user repeats to input, strengthened user's experience effect.The setting of the confidence level of resources bank also makes this system can learn user's preference, makes the result of automatic form filling more meet user's demand.
Description of drawings
Fig. 1 is list intelligent autofill method synoptic diagram of the present invention;
Fig. 2 is the implementing procedure figure of the embodiment of the invention;
Fig. 3 is the invention process case new table master drawing;
Fig. 4 is the invention process case new table automatic filling design sketch.
Embodiment
Below in conjunction with accompanying drawing the specific embodiment of the present invention is described in detail.
As shown in Figure 1, a kind of intelligent web Auto Filling Forms method comprises semantic base and resources bank, and the method may further comprise the steps:
1) extracts tag name in the list, be normalized to the standard name of described semantic base;
2) search described resources bank according to described standard name, select described candidate value to fill in a form;
3) select described candidate value to fill in a form.
A kind of intelligent web Auto Filling Forms system is characterized in that, comprising:
Semantic base is used for the tag name of storage to the mapping set of standard name;
Resources bank is used for storage " standard name-candidate value " right set;
The normalization module for the tag name of extracting list, is normalized to the standard name of described semantic base;
Retrieval module is used for searching resources bank according to described standard name, chooses candidate value;
The module of filling in a form is used for filling in a form according to the described candidate value of selecting.
Fig. 2 is the implementing procedure figure of the embodiment of the invention, is described as follows:
Definition semantic base S0, identical with solving in the list content, but the different problems that produce of the used vocabulary of tag name, for example: new table requires to fill in the form fields of label " postcode " by name, and history is made a report in the record data of only having " postcode ", semantic base is used for realizing both normalizings, is about to tag name " postcode " and is mapped on the standard name " postcode ".The present invention adopts semantic base to come the storage tags name to the set of the mapping of standard name.Semantic base possesses following feature:
1) tag name can be " standard name tag name 1| tag name 2| ... " to the mapping structure of standard name, as: " phone number " " mobile phone | mobile phone | Mobile Phone "; Also can be " standard name tag name 1 ", " standard name tag name 2 ", the set of " standard name ... ".
2) tag name can be with probability to the mapping of standard name.For example: " office telephone telephone number-[40%] ", " mobile phone telephone number-[30%] " expression history is made a report in the record, and " telephone number " has respectively 40%, 30% situation is " office telephone " and " mobile phone ".
3) tag name is to support the regular expression form.For example: 1-3 space can occur between " posting * [1-3] compiles " expression postcode.
4) tag name can manual mode be set up and is safeguarded to the mapping of standard name in the semantic base, also can use the method for the machine learning such as decision tree, Bayes, SVM, hidden Markov model to fill the record learning from history and obtain.
The net result of resources bank structure S1 is resources bank S3, its objective is as filling in new table S2 to do the data preparation.Be not to fill in new table S2 all will carry out resources bank structure S1 before at every turn.Only have and add fashionable when new reference documents or the new data of making a report on, just be necessary to re-construct resources bank, be used for constructing resources bank reference documents and historical make a report on record must be about the active user, because other people data do not have reference value to the active user.The implementation step of S1 is:
Select reference documents S11 or historical form padding data S12.The multiplex data source can be that history is made a report on list record S12, also can be the reference documents S11 of the forms such as Word, Excel, Text.For example, during recruitment website registration personal information, just can use the resume document of oneself as the multiplex data source, in order to realize Auto-writing, this module of the present invention will ask the user to upload resume as the multiplex data source.And for example: in the transacters such as monthly magazine, historical monthly magazine data are exactly the multiplex data source, wherein, the repeating parts such as unit information will be by at from now on the middle Auto-writing of making a report on, and module of the present invention will select active user's history to make a report on record as the multiplex data source.
The extraction S13 that " tag name-data value " is right; Its concrete steps are:
When the multiplex data source of selecting is that history is when making a report on list, according to active user's identity information or the project label under the current list, process background data base corresponding to list, field name and the field value of selecting the active user to fill in a form, right as " tag name-data value ".
When the multiplex data source of selecting is Word document list data or Excel document list data, at first, detect the position that tag name occurs in the reference documents according to the tag name in the semantic base, judge the distribution situation of label in form.Secondly, summarizing that label distributes is to embark on journey or becomes column distribution, and embarking on journey distribution refers to that the content of full line in the form is tag name more than 50%; Become column distribution to refer to that the content of a permutation in the form is tag name more than 50%.Data corresponding to the next line of the tag name that again, will embark on journey are as value.As: lastrow is " name | sex | age " in the form, next behavior " Zhang San | the man | 25 " (wherein " | " is cell separator in the table), then three " tag name-data values " of structure " name-Zhang San ", " sex-man ", " age-25 " are right.To becoming the label of row, the data of next column are as value.As: in the form lastrow be " name | Zhang San | sex | the man ", the situation of next behavior " age | 25| is national | the Chinese ", find that wherein first row and the 3rd is classified tag name as, then structure " name-Zhang San ", " sex-man ", " age-25 ", " nationality-Han " four " tag name-data values " are right.If the multiplex data source is word document or the text document of free text, it is right then to utilize in the natural language processing information extraction technique to obtain one group " tag name-data value ".
Semantic label normalization S14.According to semantic base S0, the tag name of " tag name-data value " centering that step S13 is obtained is normalized into standard name, and with the probability propagation in the semantic base to " standard name-candidate value " centering.For example, establishing has Semantic mapping " office telephone telephone number-[40%] " in the semantic base, " mobile phone telephone number-[20%] ", " mobile phone mobile phone-[30%] " three semantic knowledges." tag name-data value " learnt by step S13 is to there being " mobile phone-1111 ", " telephone number-2222 ", " mobile phone-1111 ", carry out normalized " mobile phone-1111-[1.0] " arranged, " office telephone-2222-[0.4] ", " mobile phone-2222-[0.2] ", " mobile phone-1111-[0.3] " four " standard name-candidate value-[probability] " are right.
By normalization label statistics " standard value-candidate value " S15.All " standard name-candidate value-[probability] " after the Statistics Implementation step S14 are right, add up by " standard name " and candidate value grouping.After the statistics with " standard name-candidate value-[probability] " to being reassembled as " standard name-candidate value-confidence level " (confidence level is described as current " standard name-candidate value " to accounting for all " standard name-candidate values " of the same name to the number percent of sum, and wherein confidence level is defined as the logarithm value of weighted frequency).For example: establishing " standard name-candidate value-[probability] " tlv triple that step S103 obtains has " mobile phone-1111-[1.0] ", and " office telephone-2222-[0.4] ", " mobile phone-2222-[0.2] ", " mobile phone-1111-[0.3] ".Then the weighted frequency of " mobile phone-1111 " is 1.3, and the weighted frequency of " office telephone-2222 " is 0.4, and the weighted frequency of " mobile phone-2222 " is 0.2.Therefore, their confidence level is respectively ln (1+1.3), ln (1+0.4), ln (1+0.2).
Screening part " standard value-candidate value ", structure resources bank S16.Implementation step S15 acquired results is screened according to confidence level, consist of data bank of the present invention.The screening principle is: to the standard name after each normalization, get confidence level greater than user-defined part, perhaps the N of confidence level maximum (be 1-3 such as the N value).For fear of noise and contingency, the suggestion many mistakes of frequency candidate value once just deposits resources bank in.The tlv triple of above-mentioned " label-value-confidence level " is stored in the resources bank for future referencely, for improving retrieval rate, can consider to set up Hash table and so on index.
The ultimate principle of filling in new table S2 such as Fig. 2 is retrieve resources storehouse S3, obtains the candidate value of each tag name in the new table, if such value exists, then selects the value of confidence level maximum to fill out in advance corresponding form fields, finishes intelligent automatic form filling.The implementation step is described as:
Extract one by one the tag name S22 of new table; Fig. 3 is the invention process case new table master drawing, and the tag name that can extract has " name ", " sex ", " personal preference ".
For new table tag name normalization S23, obtain standard name corresponding to each tag name.According to semantic resources bank S0, with the label normalization of new table, its purpose is exactly to make " tag name " name in the new table consistent with " standard name-candidate value " right " standard name " in the S3 data bank.During for next step S24 retrieve resources storehouse S3, key word can accurately mate, and this has guaranteed that also the present invention is in the accuracy of automatically identifying on " tag name ".For example: suppose that label in the semantic resources bank " hobby " is tag name " personal preference's " standard name, so, " personal preference " in the new table is normalized to standard name " hobby ".
Utilize standard name retrieve resources storehouse S24, for new table obtains one group of candidate value.With the new table standard name after the normalization as key word, retrieve resources storehouse S3 finds the operational candidate value of this standard name, at this, the user can set the number of the selecting candidate value list of filling in a form in advance again, and " standard name-candidate value " that guarantees only to get higher confidence level is to as candidate value.For example: threshold value is set gets the highest 3 of confidence level, even when certain label has a plurality of candidate value in S3, also only have confidence level the highest 3 as with reference to values.
With the candidate value of confidence level maximum, the S25 that automatically fills in a form in advance, other candidate values can be listed in the back with text mode, form fields of filling out in advance for reference, and replenish those parts of not filling out.The candidate value of the new table that obtains according to step S24, the list of automatically filling in a form in advance.Such as Fig. 4, when selecting candidate value Auto-writing list, a minute following situation is processed:
Be text box such as form fields, with directly inserting of confidence level maximum, other candidate values are presented at the back with text label;
If form fields is radio box or drop-down list, then with Total Options according to the reliability order that resources bank S3 provides, select confidence level maximum wherein as selected value;
If form fields is the multiselect frame, the Total Options that then will appear in the candidate value are all chosen.
The user revises new table S26; The user revises single label field of automatically filling in a form in advance, and replenishes those and can not realize automatic part of filling out in advance.
Submit to new table S27 and common fill in list similarly submission form process to server program.The new table data can be used as new data and deposit resources bank in, can be used as the multiplex data source that follow-up intelligence is filled.
Claims (2)
1. an intelligent web Auto Filling Forms method comprises semantic base and resources bank; It is characterized in that, may further comprise the steps:
1) extracts tag name in the list, be normalized to the standard name of described semantic base;
2) search described resources bank according to described standard name, find the candidate value of confidence level maximum;
3) select described candidate value to fill in a form;
The described tag name of described semantic base file probability is to the mapping set of described standard name;
The establishment step of described resources bank is:
201) extraction " tag name-candidate value " is right from reference documents or historical form padding data;
202) be standard name according to described semantic base with the tag name normalizing in " tag name-candidate value ", and with the probability propagation in the semantic base to " standard name-candidate value " centering, by normalization label statistics " standard value-candidate value ", " standard name-candidate value-[probability] " is right to being reassembled as " standard name-candidate value-confidence level " after the statistics;
Right to being normalized to " standard name-candidate value-confidence level ";
203) screen described " standard name-candidate value-confidence level " to being stored in described resources bank according to confidence level;
Described confidence level is that current " standard name-candidate value " is to accounting for all " standard name-candidate values " of the same name to the number percent of sum.
2. a kind of intelligent web Auto Filling Forms method according to claim 1, it is characterized in that: described step 202) and step 203) between be provided with step 204), it is right less than " standard name-candidate value-confidence level " that filter threshold value to be used for the deletion confidence level.
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