CN106844714A - A kind of knowledge base management system - Google Patents
A kind of knowledge base management system Download PDFInfo
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- CN106844714A CN106844714A CN201710068416.0A CN201710068416A CN106844714A CN 106844714 A CN106844714 A CN 106844714A CN 201710068416 A CN201710068416 A CN 201710068416A CN 106844714 A CN106844714 A CN 106844714A
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- G06F16/10—File systems; File servers
- G06F16/17—Details of further file system functions
- G06F16/174—Redundancy elimination performed by the file system
- G06F16/1744—Redundancy elimination performed by the file system using compression, e.g. sparse files
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Abstract
The invention discloses a kind of knowledge base management system, the present invention relates to a kind of knowledge base management system, with Browser/Server Mode（Browser/Server）Structure as system overall architecture, the document classification system that effective intellectual property is concluded, forms orderliness is carried out to mass file, user can carry out easily document manipulation by the system, the system puts position user experience in the first place, realize the functions such as very fast file dynamic, multi-platform file interconnection, intelligent full-text retrieval, personalized rules customization, class window-operating, it is characterized in that will efficiently be stored in Knowledge Set to server end, and web-based management pattern is used, to reach the purpose of knowledge assistance management, integrated use knowledge.System architecture aspect sets file server and Web server, it is ensured that the high efficiency of user file storage；The system will carry out effective induction-arrangement and analysis, and boosting is personal and tissue is more preferably using the realization of intellectual property.
Description
Technical field
The invention belongs to administrative skill field, more particularly to a kind of knowledge base management system.
Background technology
In our daily life and work, everyone constantly adds up in respective field, forms different levels
Process resource wealth, covers the intellectual property carrier of each type such as text, fax, figure, video, audio.Intellectual property
It is constantly accumulative during, we are also faced with some problems, because the source of intellectual property is different, purposes is different, carrier not
On an equal basis, often lead to that the time chain entanglement of intellectual property, association knowledge cannot be retrieved effectively, all kinds of knowledge cannot integrated use
The problems such as appearance, with intellectual property persistent accumulation, this problem more protrude, in the urgent need to effective means come
Typing, management, analysis, statistical knowledge wealth.
The content of the invention
The present invention provides a kind of knowledge base management system, to solve the problems of the prior art.
To achieve the above object, the technical solution adopted by the present invention is：
A kind of knowledge base management system, using Browser/Server structures as the overall architecture of system, system architecture includes
File server and Web server；
Said system also includes file basic management, the centrally stored upload of file, file link, file process, file association, text
Part is shared, rule is used, intelligent retrieval and commending system；
The file basic management carries out newly-built, duplication, stickup, shearing, renaming, deletion, online pressure to file including user
Contracting and decompression；Support Offcie documents, PDF, image, audio frequency and video and drawing class file；Support all kinds of forms of online preview
Picture, play and video, audio and browse all kinds of office documents；File download；
The centrally stored upload of file includes that user is uploaded to the batch of file and compressed package is imported；Enter style of writing in front end first
The sectioning of part, the burst for then carrying out file is uploaded；
The file link includes sending file to catalogue；
The file process after transmitting file on user, using Apache Lucene Tika algorithms, in spooler
Text extraction, extraction keyword therein can be carried out to file, or carries out the information extraction of metadata, generate the summary of file；
Detection and actual types of the text-processing also including file language form judge；Wherein, the Apache Lucene for being used
Tika algorithms, Tika is by a resolver framework, and MIME testing mechanisms, language detection, an and facade components contact is all
Component.In total, the architecture of Tika is that expansible, new resolver can easily be added and deleted.Its
Specifically include：Language testing mechanism：Supporting language recognizes that Tika has a class to be called language identifier in bag
Org.apache.tika.language and instruction identification information storehouse the inside contain algorithm of the language detection from given text.Often
When a text is passed to Tika, it will detect language therein.And receive comment file without language and
By detecting that the language adds the metadata information in this document.Its inside carries out language detection using N-gram algorithms；MIME
Testing mechanism：Tika can be according to MIME standard detection Doctypes.Acquiescence mime type detection is to use
org.apache.tika.mime.mimeTypes.It uses org.apache.tika.detect.Detector interfaces to complete
Most contents type detection；Resolver interface：Org.apache.tika.parser resolver interfaces are its parsing documents
Primary interface.Text and metadata of the interface from document is extracted, and summarize it and be ready to write resolver to external user and insert
Part.Due to using different specific resolver classes, specially each Doctype, therefore support substantial amounts of file format.These
The file format that the concrete kind of form is different provides support, either by being directly realized by logic analyzer or being parsed using outside
Device storehouse；Tika Facade classes：By realizing basic use-case, Tika as facade agency.The bottom in it is abstract Tika storehouses
Layer complexity, such as MIME testing mechanisms, resolver interface and language testing mechanism, and it is supplied to user one simply to connect
Mouthful use.
The file association, file is newly-built or after uploading, and automatic or manual is that this document generates unique number, and supports text
The batch association of shelves；
The rule is used and includes regulation engine and rule setting；
The intelligent retrieval uses Apache Lucene full-text search engine kits, sets up Solr full-text search engines, while
With reference to TF-IDF and name Entity recognition generation prediction entry；
The commending system is based on the collaborative filtering and neural network model of Mahout, forms effective retrieval system.
Wherein, the input of the recommended engine based on collaborative filtering is the history preference information of user, in Mahout it
It is modeled as Preference（Interface）.In order to optimize performance, two be given using Mahout realize class, respectively according to
User and article are assembled to user preference in itself, can thus compress the space of ID or article ID.
The Generalization bounds of the collaborative filtering that Mahout is provided, we select wherein most classical three kinds, User CF,
Item CF and Slope One；
1.User CF：
1) sets up DataModel from file, such as using FileDataModel.
2) is based on the similarity that user preference data calculates user, such as PearsonCorrelationSimilarity
（Similarity is calculated based on Pearson correlation coefficient）
ItemSimilarity is also similar：
According to the similarity calculating method set up, neighbor user is found.Here the method for looking for neighbor user, also including two kinds：It is " solid
The neighbours of fixed number amount（NearestNUserNeighborhood：The nearest-neighbors of fixed qty N are taken to each user）" and "
Similarity threshold neighbours（ThresholdUserNeighborhood：Certain limitation is based on to each user, takes similar
All users in degree thresholding are neighbours）" computational methods.Based on DataModel, UserNeighborhood and
UserSimilarity builds GenericUserBasedRecommender, realizes User CF Generalization bounds.
The realization of 2.Item CF is similar with User CF, is all based on ItemSimilarity.
3.Slope One
In big data quantity, the amount of calculation of User CF, Item CF can be very big, so as to cause to recommend efficiency poor.Therefore use
More CF Generalization bounds of lightweight：Slope One.
Neural network model is then first generated alternative file list by neutral net, then to input file（Such as Search Results
5 before candidate）List give a mark ranking, so as to by file recommendation in the top to user.
Alternative file generation is to produce a wide in range personalized recommendation candidate for user by collaborative filtering
List.Ranking neutral net is that the list of network is generated based on first candidate, there is provided finer differentiation refinement, is always reached
Recommendation hit rate higher.A series of feature of description files and user is provided by objective function, ranking network is then
Given a mark to each file according to object function.One group of file of fraction highest is just recommended to user.
The benefit of double-level neural network model can be million magnitude files for the treatment of, and ensure that the file for recommending user is
It is high-quality.Commending system employs various quantizating index, such as accuracy rate, coverage rate, ranking loss in exploitation and training stage
Etc..
The Offcie documents include the form of Word, Excel, PowerPoint, WPS, Visio；The file process
The text of middle support pdf, doc, docx, ppt, excel, txt, html, xml, zip, tar form.
The file association, user manually sets master file, and associates that its is corresponding from file, unique by file
Numbering, realizes that file is associated with associated documents, drawing, picture and other form annexes, and a key is quick by clickthrough
Check；Auto-associating is carried out to file, video generates thumbnail, and picture is compressed, so as to be embodied as picture, audio, video
Class file increases thumbnail；File increases summary.
The sharing files include that sending mail shares, and directly shares the link of file, save borrow mailbox server
Transfer process；Share to social platform；The shared file by way of letter in station；The file of wechat platform deployment in mobile terminal
Search is shared；File server is set, physical space is locally marked as virtual file server catalogue for storage file,
FTP carries out the transmission of file, and Tomcat safeguards this virtual file server catalogue and Web server Tomcat Server
When, a virtual file server is safeguarded in Tomcat, it is that every user distributes a single file root, all uploads
File moves into library, and a storage file accesses link under each User Catalog.
Also include Table top type interface, active file is presented to user in the form of desktop shortcuts, and user is without every time
Searched in each catalogue is opened, respective document is checked by the shortcut is clicked directly on when using.
Unique number is automatically generated during files passe, the batch association of file is supported, manual association operation, user can be carried out
Master file is set, and is associated corresponding from file；The unique number automatically generated during by files passe, realize document to it is related
Document, drawing, picture and other form annexes are associated, and the key of clickthrough one is quickly checked；Sharing files have various points
Enjoy in channel, including system send mail share, directly the link of file is shared, save borrow mailbox server in turn over
Journey；The shared part of the file search of wechat platform deployment in mobile terminal.
The regulation engine, including hiding, automated back-up, transfer, filing are encrypted to file are of little use file, file
It is temporary；The rule setting function, is file required movement, condition and operation, when action triggers meet the condition of setting, system
The then operation of automatic executing rule.
The Solr full-text search engines set up in the intelligent retrieval, intelligent sequencing is carried out to retrieval result according to weight,
And it is highlighted term；And provide the user cross-language information retrieval, spell check, canonical retrieval, real-time retrieval result
With the record of entry, the Optimum Operation of assisted retrieval is realized；In retrieving, according to the automatic benefit that historical record and network boom are searched
Full behavior；User can quickly precisely file needed for retrieval carries out intelligent retrieval from magnanimity data, and retrieval result is in viewing area
Domain shows.
The commending system, the viewing area of user search result, with reference to user search result first five carry out it is parallel
Recommend, i.e. the part of " you may still want to look for ", retrieved in advance for different user is generated during user search for term
Survey entry.
Said system, also including generation user's calendar and dynamic part；
User's calendar is during the quantity of documents that user uploads recorded file calendar；File dynamic part is user according to " up and down
The mode of text lookup ", is quickly found out the file of oneself needs.
Compared with prior art, the present invention has advantages below：
The present invention extracts the wealth in user knowledge, using B/S structures as the overall architecture of system, management system is placed on
Web ends, file is stored in server end.On the basis of operation readiness, mass file is carried out effective intellectual property conclude,
The document classification system of orderliness is formed, user can carry out easily document manipulation by the system, the system is by user's body
Degree of testing is put in the first place position, realizes very fast file dynamic, multi-platform file interconnection, intelligent full-text retrieval, personalized rule fixed
The functions such as system, class window-operating.
Brief description of the drawings
Fig. 1 is system architecture diagram of the invention.
Specific embodiment
The present invention is further described with reference to embodiment.
As shown in figure 1, a kind of knowledge base management system, using Browser/Server structures as the integrated stand of system
Structure, system architecture includes file server and Web server；
Said system also includes file basic management, the centrally stored upload of file, file link, file process, file association, text
Part is shared, rule is used, intelligent retrieval and commending system；
The file basic management carries out newly-built, duplication, stickup, shearing, renaming, deletion, online pressure to file including user
Contracting and decompression；Support Offcie documents, PDF, image, audio frequency and video and drawing class file；Support all kinds of forms of online preview
Picture, play and video, audio and browse all kinds of office documents；File download；
The centrally stored upload of file includes that user is uploaded to the batch of file and compressed package is imported；Enter style of writing in front end first
The sectioning of part, the burst for then carrying out file is uploaded；
The file link includes sending file to catalogue；
The file process after transmitting file on user, using Apache Lucene Tika algorithms, in spooler
Text extraction, extraction keyword therein can be carried out to file, or carries out the information extraction of metadata, generate the summary of file；
Detection and actual types of the text-processing also including file language form judge；Wherein, the Apache Lucene for being used
Tika algorithms, Tika is by a resolver framework, and MIME testing mechanisms, language detection, an and facade components contact is all
Component.In total, the architecture of Tika is that expansible, new resolver can easily be added and deleted.Its
Specifically include：Language testing mechanism：Supporting language recognizes that Tika has a class to be called language identifier in bag
Org.apache.tika.language and instruction identification information storehouse the inside contain algorithm of the language detection from given text.Often
When a text is passed to Tika, it will detect language therein.And receive comment file without language and
By detecting that the language adds the metadata information in this document.Its inside carries out language detection using N-gram algorithms；MIME
Testing mechanism：Tika can be according to MIME standard detection Doctypes.Acquiescence mime type detection is to use
org.apache.tika.mime.mimeTypes.It uses org.apache.tika.detect.Detector interfaces to complete
Most contents type detection；Resolver interface：Org.apache.tika.parser resolver interfaces are its parsing documents
Primary interface.Text and metadata of the interface from document is extracted, and summarize it and be ready to write resolver to external user and insert
Part.Due to using different specific resolver classes, specially each Doctype, therefore support substantial amounts of file format.These
The file format that the concrete kind of form is different provides support, either by being directly realized by logic analyzer or being parsed using outside
Device storehouse；Tika Facade classes：By realizing basic use-case, Tika as facade agency.The bottom in it is abstract Tika storehouses
Layer complexity, such as MIME testing mechanisms, resolver interface and language testing mechanism, and it is supplied to user one simply to connect
Mouthful use.
The file association, file is newly-built or after uploading, and automatic or manual is that this document generates unique number, and supports text
The batch association of shelves；
The rule is used and includes regulation engine and rule setting；
The intelligent retrieval uses Apache Lucene full-text search engine kits, sets up Solr full-text search engines, while
With reference to TF-IDF and name Entity recognition generation prediction entry；
The commending system is based on the collaborative filtering and neural network model of Mahout, forms effective retrieval system.
Wherein, the input of the recommended engine based on collaborative filtering is the history preference information of user, in Mahout it
It is modeled as Preference（Interface）.In order to optimize performance, two be given using Mahout realize class, respectively according to
User and article are assembled to user preference in itself, can thus compress the space of ID or article ID.
The Generalization bounds of the collaborative filtering that Mahout is provided, we select wherein most classical three kinds, User CF,
Item CF and Slope One；
1.User CF：
1) sets up DataModel from file, such as using FileDataModel.
2) is based on the similarity that user preference data calculates user, such as PearsonCorrelationSimilarity
（Similarity is calculated based on Pearson correlation coefficient）
ItemSimilarity is also similar：
According to the similarity calculating method set up, neighbor user is found.Here the method for looking for neighbor user, also including two kinds：It is " solid
The neighbours of fixed number amount（NearestNUserNeighborhood：The nearest-neighbors of fixed qty N are taken to each user）" and "
Similarity threshold neighbours（ThresholdUserNeighborhood：Certain limitation is based on to each user, takes similar
All users in degree thresholding are neighbours）" computational methods.Based on DataModel, UserNeighborhood and
UserSimilarity builds GenericUserBasedRecommender, realizes User CF Generalization bounds.
The realization of 2.Item CF is similar with User CF, is all based on ItemSimilarity.
3.Slope One
In big data quantity, the amount of calculation of User CF, Item CF can be very big, so as to cause to recommend efficiency poor.Therefore use
More CF Generalization bounds of lightweight：Slope One.
Neural network model is then first generated alternative file list by neutral net, then to input file（Such as Search Results
5 before candidate）List give a mark ranking, so as to by file recommendation in the top to user.
Alternative file generation is to produce a wide in range personalized recommendation candidate for user by collaborative filtering
List.Ranking neutral net is that the list of network is generated based on first candidate, there is provided finer differentiation refinement, is always reached
Recommendation hit rate higher.A series of feature of description files and user is provided by objective function, ranking network is then
Given a mark to each file according to object function.One group of file of fraction highest is just recommended to user.
The benefit of double-level neural network model can be million magnitude files for the treatment of, and ensure that the file for recommending user is
It is high-quality.Commending system employs various quantizating index, such as accuracy rate, coverage rate, ranking loss in exploitation and training stage
Etc..
The Offcie documents include the form of Word, Excel, PowerPoint, WPS, Visio；The file process
The text of middle support pdf, doc, docx, ppt, excel, txt, html, xml, zip, tar form.
The file association, user manually sets master file, and associates that its is corresponding from file, unique by file
Numbering, realizes that file is associated with associated documents, drawing, picture and other form annexes, and a key is quick by clickthrough
Check；Auto-associating is carried out to file, video generates thumbnail, and picture is compressed, so as to be embodied as picture, audio, video
Class file increases thumbnail；File increases summary.
The sharing files include that sending mail shares, and directly shares the link of file, save borrow mailbox server
Transfer process；Share to social platform；The shared file by way of letter in station；The file of wechat platform deployment in mobile terminal
Search is shared；File server is set, physical space is locally marked as virtual file server catalogue for storage file,
FTP carries out the transmission of file, and Tomcat safeguards this virtual file server catalogue and Web server Tomcat Server
When, a virtual file server is safeguarded in Tomcat, it is that every user distributes a single file root, all uploads
File moves into library, and a storage file accesses link under each User Catalog.
Also include Table top type interface, active file is presented to user in the form of desktop shortcuts, and user is without every time
Searched in each catalogue is opened, respective document is checked by the shortcut is clicked directly on when using.
Unique number is automatically generated during files passe, the batch association of file is supported, manual association operation, user can be carried out
Master file is set, and is associated corresponding from file；The unique number automatically generated during by files passe, realize document to it is related
Document, drawing, picture and other form annexes are associated, and the key of clickthrough one is quickly checked；Sharing files have various points
Enjoy in channel, including system send mail share, directly the link of file is shared, save borrow mailbox server in turn over
Journey；The shared part of the file search of wechat platform deployment in mobile terminal.
The regulation engine, including hiding, automated back-up, transfer, filing are encrypted to file are of little use file, file
It is temporary；The rule setting function, is file required movement, condition and operation, when action triggers meet the condition of setting, system
The then operation of automatic executing rule.
The Solr full-text search engines set up in the intelligent retrieval, intelligent sequencing is carried out to retrieval result according to weight,
And it is highlighted term；And provide the user cross-language information retrieval, spell check, canonical retrieval, real-time retrieval result
With the record of entry, the Optimum Operation of assisted retrieval is realized；In retrieving, according to the automatic benefit that historical record and network boom are searched
Full behavior；User can quickly precisely file needed for retrieval carries out intelligent retrieval from magnanimity data, and retrieval result is in viewing area
Domain shows.
The commending system, the viewing area of user search result, with reference to user search result first five carry out it is parallel
Recommend, i.e. the part of " you may still want to look for ", retrieved in advance for different user is generated during user search for term
Survey entry.
Said system, also including generation user's calendar and dynamic part；
User's calendar is during the quantity of documents that user uploads recorded file calendar；File dynamic part is user according to " up and down
The mode of text lookup ", is quickly found out the file of oneself needs.
A kind of knowledge base management system, including KBMS（WEB server）, KBMS（WEB server）With file server
（Tomcat）Communication, KBMS（WEB server）, database（Mysql）, index database（Solr）Datacycle is sequentially formed, is passed through
KBMS（WEB server）Full-text search, intelligent retrieval, prediction entry, commending system, batch upload, text extraction can be carried out
（Participle）, document associations, file, mail, exterior chain, desktop, regulation engine are transmitted/received, wherein file is extracted（Participle）Including document
Summary, keyword and name Entity recognition.
First, file basic management
User can carry out the operation such as newly-built, duplication, stickup, shearing, renaming, deletion to file, and basic pipe is carried out to file
Reason operation；Using PageOffice local component, realize line compression and the decompression of file, support Office documents, PDF,
The all types of files such as image, audio frequency and video and drawing；Support the picture of all kinds of forms of online preview, play video, audio and clear
Look at all kinds of Office files, the form such as including Word, Excel, PowerPoint, WPS, Visio.And for the use of user
Behavior, the regulation engine of coupling system can carry out further management operation to file.
In addition the system also provides the function of file download, and user can freely download the file in oneself knowledge base,
The shared file of other users can also be downloaded.
2nd, the centrally stored upload of file
For large batch of file, the system is supplied to the functions such as the batch of user file is uploaded and compressed package is imported, and exists first
Front end carries out the sectioning of file, and the burst for then carrying out file is uploaded, and while uploading speed is accelerated, can effectively be reduced
Because the file that suspension brings loses mistake during files passe.
3rd, document links
Document links function is supported to send document to common document, and user in each catalogue is opened without searching every time；This
System provides the user the interface of desktop form（Class viewfinder operation interface）, active file represents in the form of desktop shortcuts
To user, user checks respective document without being searched in each catalogue is opened every time by only need to clicking on the shortcut.
On the other hand, the system is supported to send document to catalogue, facilitates other users to consult.
4th, text-processing
After the upper transmitting file of user, using Apache Lucene Tika algorithms, the spooler of the system can be to text
File carries out text extraction, supports the common formats such as pdf, doc, docx, ppt, excel, txt, html, xml, zip, tar
Text, extracts keyword therein, for files such as video, audios, carries out the information extraction of metadata, and using life
Name Entity recognition（Stamford NER bags）, Chinese word segmentation（IKAnalyzer Chinese word segmentation bags）Etc. the summary that mode generates file.Also
Detection and actual types including file language form judge.
5th, document associations
Document it is newly-built or upload after, the system can automatic or manual be file generated unique number, support document batch association.
Operation associated aspect, user can manually set master file, and associate corresponding from file, facilitate user preferably to make
Use file.By file unique number, realize that document is associated with relevant documentation, drawing, picture and other form annexes, point
A key is quickly checked by hitting link.
In addition to carrying out manual association file, the system can carry out auto-associating to the file format of user.Can be
Video file generates thumbnail, and also picture file can be compressed, and contracting is increased so as to be embodied as picture, sound, video class file
Sketch map.In addition the system just can also increase summary for Miscellaneous Documents.
6th, sharing files
Four kinds of the system offer file share channel, including transmission mail is shared, and directly shares the link of file, saves and borrows
With the transfer process of mailbox server；Can share to social platform, cover the platforms such as QQ, wechat, microblogging；Also can be by letter in station
Mode shared file；In mobile terminal, the shared part of the file search of wechat platform deployment, can solve user computer not in body
Side but need to use the difficulty of file.Sharing files function above, can help user to carry out maximized file-sharing behaviour
Make.
7th, rule is used
The regulation engine that the system is set, hiding, automated back-up, transfer, filing can be encrypted to file and be of little use file, text
The operations such as the temporary setting of part.Except engine section, the system is supplied to the user a series of rule setting function is carried out to file,
It is file required movement（Combination）, condition and operation, when action triggers meet the condition of setting, system then automatic executing rule
Operation, makes the more hommization of our system.
At present, the main rule that the system is provided has：
（1）Thumbnail is generated according to Windows task schedulings；
（2）According to user-defined rule, specified file regularly is sent to specified mailbox, rule may include time, receiver
, the people that makes a copy for, send file, word content etc.；
（3）Can grow interior no file of fixing time by one and be put into knowledge base recycle bin according to user's request；
（4）For the file for uploading sets User Defined tag along sort or system default filing label；
（5）File encryption is hidden, and the file that need to be protected is placed into a file space for encryption, need to be input into during access independent close
Code；
（6）File association, user can customize the unfolding mode of active file, check pattern；
（7）User can choose whether automated back-up, prevent account from maliciously being deleted, and can effectively recover file；
（8）File terminal, in the setting file temporary cycle, saves space, and can be prevented effectively from disposable file with periodic cleaning
Generation；
（9）Default access rule when file generated is linked, such as access rights and life cycle, the version of enhancing user knowledge are set
Power and security；
（10）The unlatching of user-defined file recommendation function, faster more accurate acquisition recommends knowledge in retrieval to help user；
（11）User can customize file content that the form of expression of document --- -- attribute, keyword, system are extracted etc.
Aspect, facilitates user's locating file.
8th, intelligent retrieval
The system can carry out full-text search operation based on Solr, with database as source, set up index database, and inquiry velocity reaches million
Bar/millisecond, weight is calculated using TF-IDF, carries out intelligent sequencing according to weight to retrieval result, and make term highlighted aobvious
Show；Cross-language information retrieval, spell check, canonical retrieval that the system is provided the user（For professional person）, real-time retrieval
The functions such as the record of result and entry, realize the Optimum Operation of assisted retrieval；In retrieving, according to historical record and network
The auto-complete behavior that heat is searched, with reference to our commending system, preferably experience is brought to user.Allow users to it is quick from
Precisely file needed for retrieval carries out intelligent search in magnanimity data, and by being received information after search operaqtion, being judged,
The knowledge hierarchy of oneself is formed after extracting, analyze and summarizing.
9th, commending system
In the viewing area of user search result, the system is based on " collaborative filtering（Mahout）And neural network model
（RapidMiner）" algorithm, first five with reference to user search result carry out parallel recommendation, the i.e. portion of " you may still want to look for "
Point, retrieved for different user generation during user search for term and predict entry.
Tenth, user's calendar and dynamic part
The operation such as use to help user to carry out more preferable ff, the quantity of documents that user uploads recorded text by the system
In part calendar；In file dynamic part, user can be quickly found out the file of oneself needs according to the mode of " context lookup ".
The above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of knowledge base management system, it is characterised in that：Using Browser/Server structures as the overall architecture of system,
System architecture includes file server and Web server；
Also include file basic management, the centrally stored upload of file, file link, file process, file association, sharing files,
Rule use, intelligent retrieval and commending system；
The file basic management carries out newly-built, duplication, stickup, shearing, renaming, deletion, online pressure to file including user
Contracting and decompression；Support Offcie documents, PDF, image, audio frequency and video and drawing class file；Support all kinds of forms of online preview
Picture, play and video, audio and browse all kinds of office documents；File download；
The centrally stored upload of file includes that user is uploaded to the batch of file and compressed package is imported；Enter style of writing in front end first
The sectioning of part, the burst for then carrying out file is uploaded；
The file link includes sending file to catalogue；
The file process, using Apache Lucene Tika algorithms, text is carried out to file after transmitting file on user
Extract, extraction keyword therein, or carry out the information extraction of metadata, generate the summary of file；Text-processing also includes text
The detection of part language form and actual types judge；
The file association, file is newly-built or after uploading, and automatic or manual is that this document generates unique number, and supports document
Batch is associated；
The rule is used and includes regulation engine and rule setting；
The intelligent retrieval uses Apache Lucene full-text search engine kits, sets up Solr full-text search engines, while
With reference to TF-IDF and name Entity recognition generation prediction entry；
The commending system is based on the collaborative filtering and neural network model of Mahout, forms effective retrieval system.
2. knowledge base management system according to claim 1, it is characterised in that：The Offcie documents include Word,
The form of Excel, PowerPoint, WPS, Visio；In the file process support pdf, doc, docx, ppt, excel,
The text of txt, html, xml, zip, tar form.
3. knowledge base management system according to claim 1, it is characterised in that：The file association, user is manually
Master file is set, and associates that its is corresponding from file, by file unique number, realize file and associated documents, drawing, picture
And other form annexes are associated, a key is quickly checked by clickthrough；Auto-associating, video generation contracting are carried out to file
Sketch map, picture is compressed, and thumbnail is increased so as to be embodied as picture, audio, video class file；File increases summary.
4. knowledge base management system according to claim 1, it is characterised in that：The sharing files include sending mail point
Enjoy, directly share the link of file, save the transfer process for borrowing mailbox server；Share to social platform；By in station
The mode shared file of letter；In mobile terminal, the file search of wechat platform deployment is shared；File server is set, thing is locally marked
Reason space is used for storage file as virtual file server catalogue, and FTP carries out the transmission of file, and Tomcat is empty to safeguard this
When intending file server catalogue and Web server Tomcat Server, a virtual file server is safeguarded in Tomcat, be
Every user distributes a single file root, and all upper transmitting files move into library, the lower storage of each User Catalog
File access is linked.
5. knowledge base management system according to claim 1, it is characterised in that：Also include Table top type interface, active file
User is presented in the form of desktop shortcuts, user is without lookup, direct point when using in each catalogue is opened every time
Respective document is checked by hitting the shortcut.
6. knowledge base management system according to claim 1, it is characterised in that：Unique volume is automatically generated during files passe
Number, the batch association of file is supported, manual association operation can be carried out, user sets master file, and associates corresponding from file；It is logical
The unique number automatically generated during files passe is crossed, realizes that document is carried out with relevant documentation, drawing, picture and other form annexes
Association, the key of clickthrough one is quickly checked；Sharing files have it is various share in channel, including system send mail share, directly
Connect and share the link of file, save the transfer process for borrowing mailbox server；In mobile terminal, the file of wechat platform deployment is searched
Rope shares part.
7. knowledge base management system according to claim 1, it is characterised in that：The regulation engine, including file is entered
Be of little use file, file of row enciphering hiding, automated back-up, transfer, filing is kept in；The rule setting function, is that file is specified
Action, condition and operation, when action triggers meet the condition of setting, the operation of system then automatic executing rule.
8. knowledge base management system according to claim 1, it is characterised in that：The Solr set up in the intelligent retrieval is complete
Literary search engine, carries out intelligent sequencing, and be highlighted term to retrieval result according to weight；And provide the user across language
The record of speech information retrieval, spell check, canonical retrieval, real-time retrieval result and entry, realizes the Optimum Operation of assisted retrieval；
In retrieving, according to the auto-complete behavior that historical record and network boom are searched；User can be quickly accurate from magnanimity data
File needed for retrieval carries out intelligent retrieval, and retrieval result shows in viewing area.
9. knowledge base management system according to claim 8, it is characterised in that：The commending system, user search result
Viewing area, first five with reference to user search result carries out parallel recommendation, the i.e. part of " you may still want to look for ", for inspection
Rope word is different user generation retrieval prediction entry during user search.
10. knowledge base management system according to claim 1, it is characterised in that：Also include generation user's calendar and dynamic
Part；
User's calendar is during the quantity of documents that user uploads recorded file calendar；File dynamic part is user according to " up and down
The mode of text lookup ", is quickly found out the file of oneself needs.
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