CN102043793A - Knowledge-service-oriented recommendation method - Google Patents
Knowledge-service-oriented recommendation method Download PDFInfo
- Publication number
- CN102043793A CN102043793A CN2009101968729A CN200910196872A CN102043793A CN 102043793 A CN102043793 A CN 102043793A CN 2009101968729 A CN2009101968729 A CN 2009101968729A CN 200910196872 A CN200910196872 A CN 200910196872A CN 102043793 A CN102043793 A CN 102043793A
- Authority
- CN
- China
- Prior art keywords
- user
- varchat
- document
- recommendation
- processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to a knowledge-service-oriented recommendation method. A system prototype comprises four parts: (1) processing and conversion of a domain ontology OWL (Ontology Web Language) file; (2) description and processing of document resources; (3) user-system interaction; and (4) system recommendation preprocessing. Parts (1), (2) and (4) mainly belong to background processing, and the part (3) is foreground use real-time dynamic interaction; and after the domain ontologies OntoAvion is stored in a computer in the mode of the OWL file after being edited by Protege. The recommendation method has the beneficial effect of finishing multiple functions from resource processing and user interaction to visual navigation, and individual recommendation and processing, and has certain originality and advancement.
Description
[technical field]
The present invention relates to computer realm, particularly a kind of recommend method towards knowledge services can be finished from resources processing, user interactions and handle many-sided function to visual navigation, personalized recommendation, has certain originality and advanced.
[background technology]
Along with advent of Knowledge, the effect of knowledge in economic growth becomes increasingly conspicuous.Management great master Peter De Luke points out that in the era of knowledge-driven economy, knowledge will replace soil, labour, capital, equipment, become most important production factors.Transmission of knowledge, innovation and application become the major impetus that promotes social progress, and knowledge innovation and service innovation ability have become the core competitiveness key element of organizations and individuals' development.As the books information industry of brain industry important component part, for the correlative study of knowledge services with explore and become focus, the development trend from the information service to the knowledge services has become common recognition.On method of service and service feature, knowledge services emphasizes the user oriented target drives more, towards the service of knowledge content, emphasize the increment of the solution of customer problem and knowledge, thus its service that provides be specialized and personalized service, be from the innovation of advocating peace service, be the service of dynamic integrity.
Construction of information resources, information retrieval technique and retrieval service systematic research are the important component parts of knowledge services system with using, and they provide important support and guarantee for knowledge and information requirement, promotion country and the social development in science and technology innovation of satisfying the user.Yet traditional information retrieval service system exists the problem of " service personalization disappearance " and " information semantic disappearance " in user interaction process, and promptly interactive interface and retrieval mode " thousand people one side " lack the specific aim that the user serves; Query processing is based on simple literal coupling, lacks semantic processes and understandability, and search efficiency is low, thereby presents many deficiencies under the developing state of knowledge services, need to seek new technical scheme with better service in user and society.How to find and to satisfy user's knowledge requirement better, how to carry out tissue, processing, retrieval, transmission and the utilization of various information resource from the aspect of knowledge content, how the uses advanced knowledge technology carries out function expansion and transformation on the basis of conventional information retrieval and information service system, to promote its knowledge services ability, be current significant and the problem that needs solution.
Information retrieval is meant utilizes certain searching algorithm, by means of specific gopher, and at user's Search Requirement, obtains the process of useful information from structuring or non-structured data.It is three aspects that information retrieval process can be portrayed: the storage of information and tissue, the retrieval of information, the displaying of information.The development course of information retrieval has experienced manual information retrieval, computer search networking, the intelligent development stage up till now, comprises data retrieval, text retrieval and multimedia retrieval etc., typically refers to document information retrieval.
For intelligent recommendation system towards knowledge services, the present invention with this systematic naming method be IKRS (Intelligent, Knowledge service oriented Recommendation System, mean intelligence, towards the commending system of knowledge services).
[summary of the invention]
The objective of the invention is to overcome the deficiency of existing recommend method, a kind of recommend method towards knowledge services is provided, can finish from resources processing, user interactions and handle many-sided function, have certain originality and advanced to visual navigation, personalized recommendation.
The system prototype of a kind of recommend method towards knowledge services of the present invention comprises four major parts: the processing and the conversion of (1) domain knowledge body OWL file; (2) document resources is recorded and is processed; (3) user's one system interaction; (4) system recommendation pre-service; Wherein, (1), (2), (4) three parts are mainly background process, and (3) are that user's Real-time and Dynamic on foreground is mutual.
After the domain knowledge body OntoAvion of a kind of recommend method towards knowledge services of the present invention edits by Protege, be stored in the computing machine in the mode of OWL file.
A kind of recommend method towards knowledge services of the present invention is handled and changed body OWL file, and switch target has three: (1) realizes the netted visual navigation function of diagram form; (2) all the notion vocabulary Automatic Extraction in the body are come out, form the text-only file vocabulary.txt of a dictionary formula; (3) extraction of body tlv triple and relation data library storage.
Recording and processing of the document resources of a kind of recommend method towards knowledge services of the present invention mainly comprises two aspects: (1) is recording processing, with pairing shelves numbering of document resources, document title, author, subject key words, make a summary, deliver periodical, deliver term and processing entry time etc. and record data and be entered in the database, set up the document resources storehouse, document resources processing entry time is as the foundation of up-to-date resource recommendation, delivers the foundation that the term carries out time novelty weighting when excavating as weighted association rules; (2) concept dictionary that utilizes domain body OnioAvion to generate carries out the extraction of document concepts, and based on the notion vector of chapter position by weighting in 3: 2: 1 generation expression document content, carrying out similarity based on document concepts vector that generates and the document resources of having put in storage calculates, the lists of documents that is higher than the similarity value of cutting off from is deposited in the database, call during for similar resource recommendation.
Integrated multiple interactive elements in the User Interface of a kind of recommend method towards knowledge services of the present invention comprises multiple search channel, recommends feedback tabulation and visual enquiry navigation; In reciprocal process, user's log-on message, enquirement information, the information of browsing and download document resources all can be followed the tracks of by system's implicit expression and automatically record, the data message of noting can carry out statistical study, carries out the generation of data mining and recommendation preprocessed data simultaneously.
The recommendation pre-service of a kind of recommend method towards knowledge services of the present invention comprises three aspects: (1) weighted association rules excavates, find the relevance between the resource object in the user capture process, realization is recommended based on the weighted association rules of resource novelty is collaborative, when the user browsed certain resource object, other resources that will have highly related recommendation were recommended; (2) find and obtain the thin interest of user, calculate by the similarity of user interest and document resources, pre-service obtains each thin interest resource recommendation tabulation of user; (3) seek thin interest similar users, work in coordination with recommendation to the active user based on similar users group's access resources statistics.
A kind of recommend method towards knowledge services of the present invention is in design and implementation procedure, should follow following principle: (1) keeps the independence of each module, all modules of IKRS can be combined into a system, also can independently constitute an independent system separately; (2) keep code reuse as much as possible, when carrying out real application systems exploitation or articulating, but keep the change scope of code reuse reduction system with other system; (3) on the framework of system in strict accordance with the requirement of MVC, data storage, User and processing logic are separated.
[description of drawings]
The modular design figure of Fig. 1 IKRS system prototype;
[embodiment]
Below by specific embodiment technical scheme of the present invention is described in detail.
The development platform and the instrument of a kind of recommend method towards knowledge services of the present invention:
In IKRS prototype system exploitation,, determined with the Eclipse Integrated Development Environment the Java environment under and open source code basis, mainly based on following reason as system's realization according to the actual needs of using:
At first, the user is network-oriented and system independence to the requirement of retrieval, selective maturation, the healthy and strong as far as possible language of this feasible exploitation, and Java uses and is independent of the language of underlying operating system as a kind of network-oriented, its application, application maturity are admitted by vast program developer, thereby are adopted by the present invention.
The second, the cardinal rule of each modular design of system is whole opening, portability and extensibility, and this makes the present invention have to give priority to aspect developing instrument.And portable, can expand, the open source code basic design philosophy of Eclipse just because its maturation, stalwartness and graceful design have just brought shock in the open source code field once issuing
The influence of property.On the one hand, Eclipse can be used for carrying out software development, compares with the commercial software development platform also to have suitable advantage, and the special management of Eclipse source code and development organizations-Eclipse association are arranged, having absorbed numerous software companys provides feature card for it, and code resource is very abundant; On the other hand, Eclipse also can together issue and whole integrating with final products.
As shown in table 1, listed the main development tools and relevant Java software package that adopt in the IKRS prototype system performance history.
The main development tools and the software package of table 1lKRS prototype system
| Title | The purposes brief introduction |
| JDK1.5.0 and Eclipse3.2.0 | IDE Integrated Development Environment based on Java provides the plug-in type development scheme |
| Apache?Tomcat5.5 | Web server and JavaServlet container are supported Windows and Linux platform |
| MySOL?Server5.0 | Relational database server provides MySQL Workbench as management tool |
| Protege3.2.1 | Integrated, the visual body edit tool that Stanford University releases |
| Jena2.2 and Prot é g é OWL API | Be used for the software package of body OWL reasoning inquiry, the exploitation calling interface is provided |
| SWT3.2 and Jface3.2 | Based on the visual forms of Java system, the software package of control layout |
At present, in the IKRS prototype system, 23 database tables have been comprised altogether.Below in conjunction with IKRS modular structure Fig. 1 of front, list some main database table structures in the system handles.
(1) ontologies is handled and conversion Relational database table.
Table 2 Ontological concept triple table Tab_ontoCCR
| Field name | Field type | The field implication | Major key whether | Remarks |
| seqnum | long | Sequence number | Be | |
| maincept | Varchat(50) | The subject notion | ||
| objcept | Varchat(50) | The object notion |
| relation | Varchat(50) | Conceptual relation | ||
| ontoversion | Varchat(50) | Body version number |
Table 3 conceptual relation weight table Tab_ontoRW
| Field name | Field type | The field implication | Major key whether | Remarks |
| seqnum | int | Sequence number | Be | |
| relaname | Varchat(50) | The conceptual relation name | Not reproducible | |
| relaweight | float | Concern semantic similarity | ||
| ontoversion | Varchat(50) | Body version number |
(2) the Relational database table is recorded and processed to document.
Table 4 biliographic description table Tab_biboquote
| Field name | Field type | The field implication | Major key whether | Remarks |
| docid | Varchat(20) | The article numbering | Be | |
| docauthor | Varchat(100) | The article author | ||
| docjournal | Varchat(50) | The place journal title | ||
| docsession | Varchat(20) | Publish the term | ‘2006-01’ | |
| doctitle | Varchat(200) | Article title | ||
| dockeyw | Varchat(200) | Keyword | ||
| docabstr | Varchat(1000) | Article abstract | ||
| docpath | Varchat(300) | Article hard disk path | ||
| quotetime | Varchat(20) | Record the time | ‘20060101123055’ |
Table 5 document is taken out term vector table Tab_bibovector
| Field name | Field type | The field implication | Major key whether | Remarks |
| docid | Varchat(20) | The article numbering | Be | |
| vecoftitle | Varchat(100) | Title is taken out term vector | ||
| vecofkeyw | Varchat(100) | Term vector taken out in keyword | ||
| vecofabstr | Varchat(300) | Summary is taken out term vector | ||
| vecofall | Varchat(600) | Merge back notion vector | Vocabulary dimension＜=20 | |
| vecofext | Varchat(1000) | Expand back notion vector | Vocabulary dimension＜=50 | |
| weiofpos | Varchat(10) | The position weight | ' 3: 2: 1 ' redundant field | |
| cecoftime | Varchat(20) | Processing time | ‘2006010112355’ |
The similar kilsyth basalt Tab_bibosimilar of table 6 article
| Field name | Field type | The field implication | Major key whether | Remarks |
| docidcurr | Varchat(20) | The article numbering | Be | |
| docidold | Varchat(20) | The article numbering | Be | External key |
| similar | float | The content similar value | ||
| proctime | Varchat(20) | Processing time | ||
| prefvalue | float | Pre-set threshold value | ‘2006010112355’ |
(3) user interactions and behavior record Relational database table.
Table 7 user message table Tab_userinfo
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | |
| username | Varchat(20) | User's nature name | ||
| userbirth | Varchat(20) | User's birthday | ||
| usersex | Varchat(10) | User's sex |
| userspect | Varchat(100) | User's specialty background | ||
| userpref | Varchat(200) | The initial interest of user | ＜=10 speech | |
| prefvec | Varchat(200) | Initial interest vector | ＜=10 speech | |
| prefvecext | Varchat(500) | The notion vector expands | ＜=20 speech | |
| useraddr | Varchat(200) | The telex network address | ||
| userzipcode | Varchat(10) | User's postcode | ||
| userphone | Varchat(50) | Subscriber phone | ||
| usermail | Varchat(100) | User Email |
Table 8 user inquiry table Tab_userquery
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | External key |
| logtime | Varchat(20) | Landing time | Be | Get server time |
| user | Varchat(100) | The user puts question to | Be | ＜=10 speech |
| querytime | Varchat(20) | The enquirement time | Be | Get server time |
| queryvec | Varchat(200) | Enquirement notion vector | ＜=10 speech | |
| querysimgt | float | The correlativity threshold values | ＜＝0.3 | |
| querytype | Varchat(20) | The match retrieval type | Literal retrieval/conceptual retrieval | |
| queryitem | Varchat(20) | Put question to item | Author/keywords/concepts | |
| queryspan | Varchat(20) | The resource start-stop time | 2001-2005 |
Table 9 user's download record sheet Tab_userdown
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | External key |
| logtime | Varchat(20) | Landing time | Be | ‘2006010112355’ |
| userquery | Varchat(100) | The user puts question to | Be | ＜=5 speech |
| querytime | Varchat(20) | The enquirement time | Be | ‘2006010112355’ |
| docid | Varchat(20) | The document of downloading | Be | |
| downtime | Varchat(20) | Download time |
(4) pretreated Relational database table is recommended on the backstage.
Table 10 resource is totally downloaded scale Tab_downstac_all
| Field name | Field type | The field implication | Major key whether | Remarks |
| docid | Varchat(20) | The document of downloading | Be | |
| downstac | long | The accumulative total download time | ||
| stactime | Varchat(20) | Timing statistics | ‘20060101091030’ |
The up-to-date warehouse-in resource table of table 11 Tab_bibonew
| Field name | Field type | The field implication | Major key whether | Remarks |
| docid | Varchat(20) | The article numbering | Be | |
| docauthor | Varchat(100) | The article author | ||
| docjournal | Varchat(50) | The place journal title | ||
| docsession | Varchat(20) | Publish the term | ‘2006-01’ | |
| doctitle | Varchat(200) | Article title | ||
| dockeyw | Varchat(200) | Keyword |
| docabstr | Varchat(1000) | Article abstract | ||
| docpath | Varchat(300) | Article hard disk path | ||
| quotetime | Varchat(20) | Record the time | ‘2006010112355’ |
The recent Download History table of table 12 user Tab_downcrspan
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | External key |
| logtime | Varchat(20) | Landing time | Be | ‘2006010112355’ |
| userquery | Varchat(100) | The user puts question to | Be | ＜=5 speech |
| querytime | Varchat(20) | The enquirement time | Be | ‘2006010112355’ |
| docid | Varchat(20) | The document of downloading | Be | |
| downtime | Varchat(20) | Download time | Be |
Table 13 resource is downloaded scale Tab_downstac_crspan in the recent period
| Field name | Field type | The field implication | Major key whether | Remarks |
| docid | Varchat(20) | The document of downloading | Be | |
| downstac | Long | The accumulative total download time | ||
| stactime | Varchat(20) | Processing time | ‘20060101091030’ |
The recent inquiry table Tab_querycrspan of table 14 user
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | External key |
| logtime | Varchat(20) | Landing time | Be | Get server time |
| userquery | Varchat(100) | The user puts question to | Be | ＜=10 speech |
| querytime | Varchat(20) | The enquirement time | Be | Get server time |
| queryvec | Varchat(200) | Enquirement notion vector | ＜=10 speech | |
| querytype | Varchat(20) | The match retrieval type | Literal retrieval/conceptual retrieval | |
| queryitem | Varchat(20) | Put question to item | Author/keywords/concepts | |
| queryspan | Varchat(20) | The resource start-stop time | 2001-2005 |
Table 15 weighted association rules excavates table Tab_assorules
| Field name | Field type | The field implication | Major key whether | Remarks |
| seqnum | long | Number of regulation | Be | Unique |
| docidpre | Varchat(20) | The rule prefix | Be | |
| docidsuff | Varchat(20) | The rule suffix | Be | |
| valsupp | float | Support | ||
| valconf | float | Degree of confidence | ||
| valrecm | float | The recommendation degree | ||
| ruletime | Varchat(20) | The rule rise time | ‘20060101091030’ |
The thick vector table Tab_roughprefs of table 16 user interest
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be |
| roughprefs | Varchat(3000) | Interest is vector slightly | ＜=150 speech | |
| stactime | Varchat(20) | Processing time | ‘20060101091030’ |
The thin vector table Tab_deliprefs of table 17 user interest
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | Major key |
| deliprefno | Varchat(2) | The thin vector numbering of interest | ||
| deliprefs | Varchat(600) | Interest is vector carefully | ＜=30 speech | |
| stactime | Varchat(20) | Processing time | ‘20060101091030’ |
Table 18 user interest resource recommendation table Tab_prefrecom
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | Major key |
| deliprefno | Varchat(2) | The thin vector numbering of interest | Be | |
| delirecdocid | Varchat(20) | Thin vector is recommended document | Be | |
| recmvalue | float | The recommendation degree | ||
| valuegt | float | Recommend the bottom valve value | ||
| stactime | Varchat(20) | Processing time | ‘20060101091030’ |
The thin interest similar users table Tab_simusers of table 19
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | |
| useridsuff | Varchat(20) | User ID | ＜=30 speech | |
| simvalue | float | Similarity |
| valuegt | float | The similarity threshold values | ||
| stactime | Varchat(20) | Processing time | ‘20060101091030’ |
The collaborative recommendation tables Tab_collarecom of the thin interest similar users of table 20
| Field name | Field type | The field implication | Major key whether | Remarks |
| userid | Varchat(20) | User ID | Be | |
| recmdocid | Varchat(20) | Recommend document id | Be | |
| valuegt | float | The recommendation degree | ||
| valuegt | float | Recommend the bottom valve value | ||
| stactime | Varchat(20) | Processing time | ‘20060101091030’ |
It should be noted last that: above embodiment only is illustrative rather than definitive thereof technical scheme of the present invention, although the present invention is had been described in detail with reference to the foregoing description, those of ordinary skill in the art is to be understood that, still can make amendment or be equal to replacement the present invention, and not breaking away from any modification or partial replacement of the spirit and scope of the present invention, it all should be encompassed in the middle of the claim scope of the present invention.
Claims (6)
1. recommend method towards knowledge services, it is characterized in that: system prototype comprises four major parts:
(1) processing and the conversion of domain knowledge body OWL file; (2) document resources is recorded and is processed; (3) user-system interaction; (4) system recommendation pre-service; Wherein, (1), (2), (4) three parts are mainly background process, and (3) are that user's Real-time and Dynamic on foreground is mutual.
2. a kind of recommend method according to claim 1 towards knowledge services, it is characterized in that: recording and processing of document resources mainly comprises two aspects: (1) is recording processing, with pairing shelves numbering of document resources, the document title, the author, subject key words, summary, deliver periodical, deliver the term and the processing entry time is recorded data and is entered in the database, set up the document resources storehouse, document resources processing entry time is as the foundation of up-to-date resource recommendation, delivers the foundation that the term carries out time novelty weighting when excavating as weighted association rules; (2) concept dictionary that utilizes domain body OnioAvion to generate carries out the extraction of document concepts, and based on the notion vector of chapter position by weighting in 3: 2: 1 generation expression document content, carrying out similarity based on document concepts vector that generates and the document resources of having put in storage calculates, the lists of documents that is higher than the similarity value of cutting off from is deposited in the database, call during for similar resource recommendation.
3. a kind of recommend method towards knowledge services according to claim 1 is characterized in that: integrated multiple interactive elements in the User Interface comprises multiple search channel, recommends feedback tabulation and visual enquiry navigation; In reciprocal process, user's log-on message, enquirement information, the information of browsing and download document resources all can be followed the tracks of by system's implicit expression and automatically record, the data message of noting can carry out statistical study, carries out the generation of data mining and recommendation preprocessed data simultaneously.
4. a kind of recommend method according to claim 1 towards knowledge services, it is characterized in that: recommend pre-service to comprise three aspects: (1) weighted association rules excavates, find the relevance between the resource object in the user capture process, realization is recommended based on the weighted association rules of resource novelty is collaborative, when the user browsed certain resource object, other resources that will have highly related recommendation were recommended; (2) find and obtain the thin interest of user, calculate by the similarity of user interest and document resources, pre-service obtains each thin interest resource recommendation tabulation of user; (3) seek thin interest similar users, work in coordination with recommendation to the active user based on similar users group's access resources statistics.
5. a kind of recommend method towards knowledge services according to claim 1 is characterized in that: body OWL file is handled and changed, and switch target has three: (1) realizes the netted visual navigation function of diagram form; (2) all the notion vocabulary Automatic Extraction in the body are come out, form the text-only file vocabulary.txt of a dictionary formula; (3) extraction of body tlv triple and relation data library storage.
6. a kind of recommend method towards knowledge services according to claim 1 is characterized in that: after domain knowledge body OntoAvion edits by Protege, be stored in the computing machine in the mode of OWL file.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009101968729A CN102043793A (en) | 2009-10-09 | 2009-10-09 | Knowledge-service-oriented recommendation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009101968729A CN102043793A (en) | 2009-10-09 | 2009-10-09 | Knowledge-service-oriented recommendation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102043793A true CN102043793A (en) | 2011-05-04 |
Family
ID=43909935
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2009101968729A Pending CN102043793A (en) | 2009-10-09 | 2009-10-09 | Knowledge-service-oriented recommendation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102043793A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103635895A (en) * | 2011-06-30 | 2014-03-12 | 微软公司 | Personal long-term agent for providing multiple supportive services |
| CN104008203A (en) * | 2014-06-17 | 2014-08-27 | 浙江工商大学 | User interest discovering method with ontology situation blended in |
| CN104021198A (en) * | 2014-06-16 | 2014-09-03 | 北京理工大学 | Relational database information retrieval method and device based on ontology semantic index |
| CN104063429A (en) * | 2014-06-11 | 2014-09-24 | 深圳德协保税电子商务有限公司 | Predicting method for user behavior in e-commerce |
| CN104281617A (en) * | 2013-07-10 | 2015-01-14 | 广州中国科学院先进技术研究所 | Domain knowledge-based multilayer association rules mining method and system |
| CN105183817A (en) * | 2015-08-27 | 2015-12-23 | 北京时代焦点国际教育咨询有限责任公司 | Question bank modeling method and system based on domain-driven design |
| CN105678383A (en) * | 2016-01-04 | 2016-06-15 | 北京飞舜信息技术有限公司 | Mobile knowledge service system on the basis of ontology model |
| CN103886046B (en) * | 2014-03-11 | 2017-02-08 | 中国信息安全测评中心 | Automatic semanteme extraction method for Web data exchange |
| CN103744955B (en) * | 2014-01-04 | 2017-04-05 | 北京理工大学 | A kind of semantic query method based on Ontology Matching |
| CN108287916A (en) * | 2018-02-11 | 2018-07-17 | 北京方正阿帕比技术有限公司 | A kind of resource recommendation method |
| CN110069607A (en) * | 2017-12-14 | 2019-07-30 | 株式会社日立制作所 | For the method, apparatus of customer service, electronic equipment, computer readable storage medium |
| CN111540421A (en) * | 2020-04-19 | 2020-08-14 | 周玉娟 | Big data based intelligent medical condition monitoring method and big data medical cloud platform |
-
2009
- 2009-10-09 CN CN2009101968729A patent/CN102043793A/en active Pending
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9317834B2 (en) | 2011-06-30 | 2016-04-19 | Microsoft Technology Licensing, Llc | User computing device with personal agent program for recommending meeting a friend at a service location based on current location, travel direction, and calendar activity |
| US9569726B2 (en) | 2011-06-30 | 2017-02-14 | Microsoft Technology Licensing, Llc | Server computing device for recommending meeting a friend at a service location based on current location, travel direction, and calendar activity |
| CN103635895A (en) * | 2011-06-30 | 2014-03-12 | 微软公司 | Personal long-term agent for providing multiple supportive services |
| CN104281617A (en) * | 2013-07-10 | 2015-01-14 | 广州中国科学院先进技术研究所 | Domain knowledge-based multilayer association rules mining method and system |
| CN103744955B (en) * | 2014-01-04 | 2017-04-05 | 北京理工大学 | A kind of semantic query method based on Ontology Matching |
| CN103886046B (en) * | 2014-03-11 | 2017-02-08 | 中国信息安全测评中心 | Automatic semanteme extraction method for Web data exchange |
| CN104063429A (en) * | 2014-06-11 | 2014-09-24 | 深圳德协保税电子商务有限公司 | Predicting method for user behavior in e-commerce |
| CN104021198A (en) * | 2014-06-16 | 2014-09-03 | 北京理工大学 | Relational database information retrieval method and device based on ontology semantic index |
| CN104021198B (en) * | 2014-06-16 | 2017-09-01 | 北京理工大学 | The relational database information search method and device indexed based on Ontology |
| CN104008203B (en) * | 2014-06-17 | 2018-04-17 | 浙江工商大学 | A kind of Users' Interests Mining method for incorporating body situation |
| CN104008203A (en) * | 2014-06-17 | 2014-08-27 | 浙江工商大学 | User interest discovering method with ontology situation blended in |
| CN105183817A (en) * | 2015-08-27 | 2015-12-23 | 北京时代焦点国际教育咨询有限责任公司 | Question bank modeling method and system based on domain-driven design |
| CN105678383B (en) * | 2016-01-04 | 2018-03-27 | 北京飞舜信息技术有限公司 | Mobile knowledge service system based on ontology model |
| CN105678383A (en) * | 2016-01-04 | 2016-06-15 | 北京飞舜信息技术有限公司 | Mobile knowledge service system on the basis of ontology model |
| CN110069607A (en) * | 2017-12-14 | 2019-07-30 | 株式会社日立制作所 | For the method, apparatus of customer service, electronic equipment, computer readable storage medium |
| CN110069607B (en) * | 2017-12-14 | 2024-03-05 | 株式会社日立制作所 | Method, apparatus, electronic device, and computer-readable storage medium for customer service |
| CN108287916A (en) * | 2018-02-11 | 2018-07-17 | 北京方正阿帕比技术有限公司 | A kind of resource recommendation method |
| CN111540421A (en) * | 2020-04-19 | 2020-08-14 | 周玉娟 | Big data based intelligent medical condition monitoring method and big data medical cloud platform |
| CN111540421B (en) * | 2020-04-19 | 2021-01-08 | 深圳坐标软件集团有限公司 | Big data based intelligent medical condition monitoring method and big data medical cloud platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102043793A (en) | Knowledge-service-oriented recommendation method | |
| CN106844658B (en) | Automatic construction method and system of Chinese text knowledge graph | |
| CN102902700B (en) | Online-increment evolution topic model based automatic software classifying method | |
| CN100458795C (en) | Intelligent word input method and input method system and updating method thereof | |
| CN101655857B (en) | Method for mining data in construction regulation field based on associative regulation mining technology | |
| CN111581990B (en) | Cross-border transaction matching method and device | |
| Belwal et al. | A new graph-based extractive text summarization using keywords or topic modeling | |
| CN107798387B (en) | Knowledge service system and method suitable for full life cycle of high-end equipment | |
| CN109033284A (en) | The power information operational system database construction method of knowledge based map | |
| CN101630314A (en) | Semantic query expansion method based on domain knowledge | |
| CN103324700A (en) | Noumenon concept attribute learning method based on Web information | |
| Du et al. | An approach for selecting seed URLs of focused crawler based on user-interest ontology | |
| US20220253719A1 (en) | Schema augmentation system for exploratory research | |
| CN103425740A (en) | IOT (Internet Of Things) faced material information retrieval method based on semantic clustering | |
| CN112036178A (en) | Distribution network entity related semantic search method | |
| Srivastava et al. | Extractive multi-document text summarization using dolphin swarm optimization approach | |
| An et al. | Automatic generation of ontology from the deep web | |
| Suresh et al. | Data mining and text mining—a survey | |
| Zhang et al. | The extraction method of new logining word/term for social media based on statistics and N-increment | |
| Abu Rasheed et al. | A text extraction-based smart knowledge graph composition for integrating lessons learned during the microchip design | |
| Kanwal et al. | Adaptively intelligent meta-search engine with minimum edit distance | |
| Mahalakshmi et al. | An effective multilingual retrieval with query optimization using deep learning technique | |
| Eddamiri et al. | Graph embeddings for linked data clustering | |
| Hristoskova et al. | A graph-based disambiguation approach for construction of an expert repository from public online sources | |
| Hirchoua et al. | Topic hierarchies for knowledge capitalization using hierarchical Dirichlet processes in big data context |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| DD01 | Delivery of document by public notice |
Addressee: Lu Jianhua Document name: Notification that Application Deemed to be Withdrawn |
|
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20110504 |