CN102521244A - User data analysis system based on learning-type OWL (Ontology of Web Language) modeling - Google Patents
User data analysis system based on learning-type OWL (Ontology of Web Language) modeling Download PDFInfo
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- CN102521244A CN102521244A CN2011103576796A CN201110357679A CN102521244A CN 102521244 A CN102521244 A CN 102521244A CN 2011103576796 A CN2011103576796 A CN 2011103576796A CN 201110357679 A CN201110357679 A CN 201110357679A CN 102521244 A CN102521244 A CN 102521244A
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Abstract
The invention discloses a user data analysis system based on learning-type OWL (Ontology of Web Language) modeling. The user data analysis system is characterized by comprising a manual processing module, an inverted index base building module and an OWL model comparison module, wherein the manual processing module is used for manually building OWL knowledge models of user data and taking the OWL knowledge models as seed models; the inverted index base building module is used for acquiring user data information from an internet and converting the user data information into OWL ontology examples, taking users as ontology elements of the OWL ontology examples, and building an inverted index database for the users; and the OWL model comparison module is used for comparing the seed models with the users in the inverted index database, and compensating an attribute which belongs to the same user data into the seed models so as to perfect the seed models. According to the system disclosed by the invention, manual intervention is combined with automatic learning, so that the OWL modeling of the user data is realized, and related information searched from the internet is compensated automatically, therefore a very operable solution idea is provided for building an extensive and perfect user data computer model.
Description
Technical field
The invention belongs to field of computer technology, be specifically related to a kind of OWL modeling.
Background technology
Today, the internet has goed deep into the every nook and cranny of human society, and can predict it and will in the Development of Human Civilization process, play the part of more and more important role.The information that has magnanimity on the internet can provide various business opportunities, for example subscriber data for people; But how in the information of these magnanimity, accurately obtaining the knowledge that oneself needs is present difficulty.
Let the computer understanding internet, serve the direction that the mankind are future development thereby more intelligently from the information of magnanimity, choose appropriate information.In order to achieve this end; People have done many trials, for example: can construct the internet again with the structure of knowledge, that is: semantic internet; It mainly adopts internet Ontology Language (Ontology of Web Language, the abbreviation: OWL) set up semantic network of W3C.If everyone presses OWL and creates the internet, internet itself just becomes the computing machine structure of knowledge of " understanding " to a certain extent.Software engineers can be a series of inference rules of Computer Design and engine on this basis, on the OWL semantic network, let computing machine oneself " understanding " internet information content, and make right judgement and operation.
How utilizing the design of OWL accurately to obtain the subscriber information message that needs on the internet is the problem that the present invention will solve.
Summary of the invention
The invention provides a kind of solution of the above problems, provide a kind of semi-automatic learning type, efficiently, OWL modeling accurately.
Principle of the present invention is: a large amount of subscriber datas is arranged in the internet, all have a cover to describe to each user, comprising: user name, E-mail address, personal information, occupation, hobby, demand, or the like.Utilize search engine or internet site Accreditation System to obtain subscriber information message as much as possible; Simultaneously; Utilize semi-automatic learning type OWL modeling tool, the kind submodel of Internet user's data structure of knowledge is set up in first manual work, then; All user profile to obtaining are carried out " study ", make Internet user's data knowledge model constantly perfect.
Technical scheme of the present invention provides a kind of subscriber data analytic system based on learning type OWL modeling, it is characterized in that: it comprises that artificial treatment module, inverted index build library module and OWL model comparison module, wherein:
The artificial treatment module, the OWL knowledge model of setting up subscriber data with manually-operated mode is as kind of a submodel;
Inverted index is built library module, gathers subscriber information message from the internet and converts thereof into the OWL instances of ontology, with user's this volume elements as this OWL instances of ontology, sets up user's inverted index database;
OWL model comparison module compares the user in kind of submodel and the inverted index database, and the attribute that will belong to same subscriber data adds in kind of the submodel to improve kind of a submodel.
Preferably, it also comprises OWL ontology model storehouse, is used to store the OWL instances of ontology after kind submodel that said artificial treatment module sets up and said inverted index are built the library module conversion.
Preferably, said inverted index is built library module and is comprised with lower module:
The original document management system is responsible for gathering various subscriber information messages from the internet through search engine;
OWL body modular converter is responsible for the information translation of original document management system collection is become the OWL instances of ontology, and is deposited OWL instances of ontology storehouse in;
Refine OWL body element module, the user who refines all OWL instances of ontology in the OWL instances of ontology storehouse is as this volume elements;
Instances of ontology inverted index module is set up the inverted index of this volume elements of refining the acquisition of OWL body element module, and is deposited said inverted index database in.
Preferably, said artificial treatment module also comprises the dictionary maintenance module, be responsible for to set up and also to safeguard OWL body dictionary, and said OWL body modular converter becomes the OWL instances of ontology according to said OWL body dictionary with the information translation of original document management system collection.
Preferably; Said artificial treatment module also comprises OWL compatibility rules manual maintenance module; Be responsible for setting up and safeguarding OWL compatibility rules storehouse, said OWL model comparison module compares this volume elements in said kind of submodel and the inverted index database according to the OWL compatibility rules in the said OWL compatibility rules storehouse.
Preferably, said artificial treatment module also comprises knotty problem artificial treatment module, be responsible for to handle unusual in the said OWL model comparison module and said kind of submodel of artificial adjustment.
Preferably, said subscriber data comprises one or more of following information: address name, E-mail address, personal information, occupation, hobby and demand.
A kind of subscriber data analytic system based on learning type OWL modeling of the present invention is that manual intervention is combined with learning automatically, thereby realizes the OWL modeling to subscriber data, and automatically the relevant information of searching on the internet is replenished into.For setting up extensive and complete subscriber data computer model a kind of solution thinking that has operability is provided.It has advantages such as efficient, accurate.
Description of drawings
Fig. 1 is the principle framework figure of a kind of subscriber data analytic system based on learning type OWL modeling of the present invention.
Embodiment
Following specific embodiments of the invention is described in further detail.
As shown in Figure 1, a kind of subscriber data analytic system based on learning type OWL modeling of the present invention is by forming with the lower part:
1) original document management system Internet user's data information that come from the search engine collection or that obtain through the internet site Accreditation System;
2) OWL modular converter subscriber information message that the original document management system is provided is done the OWL conversion, and deposits the instances of ontology database in;
3) refine OWL body element module and from each instances of ontology of instances of ontology storehouse, extract this volume elements (that is: user);
4) instances of ontology inverted index module is accomplished the inverted index to this volume elements, and deposits this volume elements inverted index storehouse in;
5) OWL model comparison module; The internet structure of knowledge OWL seed knowledge model of building with manual work is the basis; Each this volume elements in this volume elements inverted index table is compared, and utilizes the compatibility rules of manual maintenance to judge that which this volume elements belongs to the same knowledge category of seed knowledge model, judges position and the meaning of a new user in Internet user's data structure of knowledge; In the time of can't judging, dish out unusually to the artificial treatment module; Knotty problem artificial treatment module is accepted unusual that OWL model comparison module dishes out, artificial adjustment model;
6) result of the legitimate result of model comparison and manual intervention delivers to the model modification module kind of a submodel is made amendment, upgraded, and deposits the OWL model bank in;
7) compatibility rules module owner machine is mutual, accomplishes the maintenance to compatibility rules, and the result deposits the compatibility rules storehouse in;
8) OWL kind submodel and OWL dictionary storehouse all are to safeguard that through " manual maintenance of OWL Ontology Modeling, dictionary " module OWL knowledge model and OWL dictionary also will be used in the OWL transfer process.
Specifically, the main flow of a kind of subscriber data analytic system based on learning type OWL modeling of the present invention is following:
1, the knowledge manager sets up the kind submodel of Internet user's data through artificial modeling tool;
2, obtain Internet user's data information through search engine or other information acquisition means, and deposit the original document management system in;
3, the conversion of OWL instances of ontology, this volume elements inverted index are done to raw information by system, and deposit this volume elements inverted index table in;
4, system utilizes the seed knowledge model of manual creation and (existing in the inverted index table) each this volume elements that system tentatively refines to carry out the model contrast, under the help of compatibility rules, discerns similar knowledge;
5, directly export to OWL model modification module to this volume elements that meets " knowledge rationally " standard, submit the knowledge that " query " arranged to artificial processing module, abandon irrelevant knowledge;
6, the knowledge manager does suitable adjustment according to the enquirement of system to knowledge model, and exports to OWL model modification module;
7, OWL model modification module is responsible for revising, upgrading the seed knowledge model;
8, continuous repeating step 1 to 7, the OWL knowledge model will be more and more perfect;
When 9, needing, artificial adjustment compatibility rules, OWL dictionary storehouse even OWL model itself.
Above embodiment is merely the present invention's a kind of embodiment wherein, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art under the prerequisite that does not break away from the present invention's design, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with accompanying claims.
Claims (7)
1. subscriber data analytic system based on learning type OWL modeling is characterized in that: it comprises that artificial treatment module, inverted index build library module and OWL model comparison module, wherein:
The artificial treatment module, the OWL knowledge model of setting up subscriber data with manually-operated mode is as kind of a submodel;
Inverted index is built library module, gathers subscriber information message from the internet and converts thereof into the OWL instances of ontology, with user's this volume elements as this OWL instances of ontology, sets up user's inverted index database;
OWL model comparison module compares the user in kind of submodel and the inverted index database, and the attribute that will belong to same subscriber data adds in kind of the submodel to improve kind of a submodel.
2. a kind of subscriber data analytic system according to claim 1 based on learning type OWL modeling; It is characterized in that: it also comprises OWL ontology model storehouse, is used to store the OWL instances of ontology after kind submodel that said artificial treatment module sets up and said inverted index are built the library module conversion.
3. a kind of subscriber data analytic system based on learning type OWL modeling according to claim 1 is characterized in that: said inverted index is built library module and is comprised with lower module:
The original document management system is responsible for gathering various subscriber information messages from the internet through search engine;
OWL body modular converter is responsible for the information translation of original document management system collection is become the OWL instances of ontology, and is deposited OWL instances of ontology storehouse in;
Refine OWL body element module, the user who refines all OWL instances of ontology in the OWL instances of ontology storehouse is as this volume elements;
Instances of ontology inverted index module is set up the inverted index of this volume elements of refining the acquisition of OWL body element module, and is deposited said inverted index database in.
4. a kind of subscriber data analytic system according to claim 3 based on learning type OWL modeling; It is characterized in that: said artificial treatment module also comprises the dictionary maintenance module; Be responsible for to set up and also to safeguard OWL body dictionary, said OWL body modular converter according to said OWL body dictionary just the information translation of original document management system collection become the OWL instances of ontology.
5. a kind of subscriber data analytic system according to claim 1 based on learning type OWL modeling; It is characterized in that: said artificial treatment module also comprises OWL compatibility rules manual maintenance module; Be responsible for setting up and safeguarding OWL compatibility rules storehouse, said OWL model comparison module compares this volume elements in said kind of submodel and the inverted index database according to the OWL compatibility rules in the said OWL compatibility rules storehouse.
6. a kind of subscriber data analytic system according to claim 1 based on learning type OWL modeling; It is characterized in that: said artificial treatment module also comprises knotty problem artificial treatment module; Be responsible for to handle unusual in the said OWL model comparison module and said kind of submodel of artificial adjustment.
7. a kind of subscriber data analytic system based on learning type OWL modeling according to claim 1, it is characterized in that: said subscriber data comprises one or more of following information: address name, E-mail address, personal information, occupation, hobby and demand.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107918618A (en) * | 2016-10-10 | 2018-04-17 | 腾讯科技（北京）有限公司 | Data processing method and device |
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| CN101582073A (en) * | 2008-12-31 | 2009-11-18 | 北京中机科海科技发展有限公司 | Intelligent retrieval system and method based on domain ontology |
| US20090287678A1 (en) * | 2008-05-14 | 2009-11-19 | International Business Machines Corporation | System and method for providing answers to questions |
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| US20090287678A1 (en) * | 2008-05-14 | 2009-11-19 | International Business Machines Corporation | System and method for providing answers to questions |
| CN101393565A (en) * | 2008-11-07 | 2009-03-25 | 北京航空航天大学 | Facing virtual museum searching method based on noumenon |
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