CN102945266A - Method for recommending multi-field products based on body query system - Google Patents
Method for recommending multi-field products based on body query system Download PDFInfo
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Abstract
The invention discloses a method for recommending multi-field products based on a body query system, which is characterized by comprising the steps of (1) inputting product information in the body query system to query, wherein the body query system guides a rule inference engine to retrieve in body libraries associated with products; and (2) demonstrating product information required by users and associated product resources to the users according to the retrieval result and prompting the users associated products. The method can solve the problem that users are hard to obtain useful information due to complex results of normal retrieval of users, and avoid erroneous decision of users.
Description
Technical field
The invention belongs to technical field of information processing, be specifically related to a kind of method of carrying out multi-field Products Show based on the Ontology Query system.
Background technology
Body be a kind of knowledge base to the description of field things, with Semantic, can be described as another kind of database.The target of body is the knowledge of catching association area, common understanding to this domain knowledge is provided, determine the vocabulary of common approval in this field, and provide the clearly definition of mutual relationship between these vocabulary (term) and vocabulary from the formalization pattern of different levels.Generally speaking, the structure body can be realized knowledge sharing to a certain degree and reuse, and the ability that improves system communication, interoperability, reliability.
The implication of data is exactly semantic.Briefly, data are with regard to is-symbol.Data itself are without any meaning, and the data that only are endowed implication can be used, and at this time data just transform for information, and the implication of data is exactly semantic.
Semanteme has the territoriality feature, and the semanteme that does not belong to any field is non-existent.Semantic Heterogeneous then refers to same thing existing difference on explaining also just is presented as the difference that same thing is understood in different field.For computer science, semanteme generally refers to the user for those explanations that is used for describing the computer representation (being symbol) of real world, and namely the user is used for contacting the approach of computer representation and real world.Semanteme is the explanation to the data symbol, and grammer then is the definition for the organization regulation between these symbols and structural relation.For the information integration field, data (do not exist or implicit destructuring and semi-structured data for pattern by pattern often, often need to before integrated, define their pattern) tissue, the access of data also obtains by acting on pattern, at this moment semanteme is exactly the implication of finger print formula element (for example class, attribute, constraint etc.), and grammer then is the structure of schema elements.
Semantic net is the Chinese of Semantic Web.Semantic net is exactly the network that can judge according to semanteme.Briefly, semantic net is a kind of intelligent network that can understand human language, and it not only can understand human language, but also can make people and interchange between the computer become light the interpersonal interchange of picture.
Semantic net is an imagination to future network, in such network, information all has been endowed clear and definite implication, machine can automatically be processed and the integrated information that can use on the net. and semantic net defines the tag format of customization and comes expression data with the dirigibility of RDF with XML, and next step needs is exactly that the netspeak (such as OWL) of a kind of Ontology is described the clear and definite implication of the term in the network documentation and the relation between them.
The architecture of semantic net is built, research to this architecture in the current international coverage does not also form a gratifying tight logical description and theoretical system, Chinese scholar is also just done concise and to the point introduction on the basis of research abroad to this architecture, does not also form the elaboration of system.
The realization of semantic net needs the support of three large gordian techniquies: XML, RDF and Ontology.XML (eXtensible Marked Language, i.e. extend markup language) can allow the informant as required, self-defining mark and attribute-name, thus make the structure of XML file can complicatedly arrive any degree.It has good data memory format and extensibility, highly structural and is convenient to the advantages such as Internet Transmission, add numerous types of data and verification scheme that its distinctive NS mechanism and XML Schema support, make it become one of gordian technique of semantic net.Present discussion about the semantic net gordian technique mainly concentrates on it RDF and the Ontology.The present invention therefore.
Summary of the invention
The object of the invention is to provide a kind of method of carrying out multi-field Products Show based on the Ontology Query system, has solved the problem such as Ontology network shortage technology application in the prior art.
In order to solve these problems of the prior art, technical scheme provided by the invention is:
A kind of method of carrying out multi-field Products Show based on the Ontology Query system is characterized in that said method comprising the steps of:
(1) input product information is inquired about in the Ontology Query system, and described Ontology Query system introducing rule-based reasoning machine is retrieved in each ontology library relevant with product;
(2) according to result for retrieval the product information of user's needs is showed user and prompting user Related product with relevant product resource.
Preferably, each ontology library relevant with product comprises product quality body, news body, product safety body, product ontology that product is relevant in the described method.
Preferably, the order of retrieval is to retrieve first the relevant news body of product in the described method step (1), and the factor of influence that obtains according to the relevant news body of product judges whether to carry out product safety body and product quality Ontology Query.
Preferably, when having factor of influence, the Ontology Query system is inquired about in product safety body and product quality body, and inquires about in product ontology in the described method step (1).
Preferably, when not having factor of influence, the Ontology Query system is only inquired about in product ontology in the described method step (1).
Preferably, the rule-based reasoning machine that loads in the described method step (1) is the Reasoner inference machine.
Technical solution of the present invention is carried out multi-field Products Show based on body, uses the characteristics of body, obtains a kind of striding and multi-field in conjunction with inference machine potential related information is carried out the method that ontology information is inquired about.
The present invention compared with prior art has following beneficial effect:
Technical solution of the present invention is carried out the inquiry of product related information by the loading rule inference machine, comprehensive by Query Result, will the important information relevant with product present and recommend the user, can avoid the result of user's normal retrieval numerous and diverse, the user is difficult to obtain Useful Information, avoids user's erroneous decision.
Description of drawings
The invention will be further described below in conjunction with drawings and Examples:
Fig. 1 is the layer of structure figure of technical solution of the present invention body;
Fig. 2 is the hierarchy chart between class and object in the body in the technical solution of the present invention;
Fig. 3 is object relationship figure in the technical solution of the present invention body;
Fig. 4 is the building-block of logic of technical solution of the present invention rule-based reasoning;
Fig. 5 is the building-block of logic of technical solution of the present invention rabid ox disease news example;
Fig. 6 is the structural drawing of technical solution of the present invention commending system;
Fig. 7 is the process flow diagram of technical solution of the present invention commending system.
Embodiment
Below in conjunction with specific embodiment such scheme is described further.Should be understood that these embodiment are not limited to limit the scope of the invention for explanation the present invention.The implementation condition that adopts among the embodiment can be done further adjustment according to the condition of concrete producer, and not marked implementation condition is generally the condition in the normal experiment.
Embodiment
In the present embodiment on the model that is based upon class-based tlv triple of body, i.e. subject (SUBJECT), object (object) and predicate (predicate), represent two objects and between certain contact.
Class in the body of the present embodiment, can look large a class is a small-sized domain body, makes up a large ontology model, class formation is as shown in Figure 1.Be illustrated in figure 2 as the relation between object in the body, inhomogeneous object links together by the ObjectProperty attribute.The object properties inheritance of ObjectProperty attribute as shown in Figure 3.
Fig. 4 has showed the logical organization of two rule-based reasonings.Wherein, rule 1 is that the people who buys commodity A often can buy commodity B, and the people of purchase commodity B often can buy commodity C, can be interested in commodity C so perhaps buy the people of commodity A, and namely commodity C becomes the recommended project for the people who has bought commodity A.
Rule 2 is if news item is relevant with a certain problem, and this problem can have influence on a kind of commodity, illustrates that then these commodity and this news is related and can be affected.Relevant (Belinkwith) of the example of news example and food-safety problem or the example of product quality problem, and these food-safety problems or product quality problem can exert an influence to commodity (Affect), and these impacts are that the user can repel for these commodity because of the quality that commodity comprise or safety problem.Such as a certain the news about certain food security, and this news relates to certain commodity, and we think that this negative news can exert an influence to commodity so.
Fig. 5 on the basis of Fig. 4 the examples show by a rabid ox disease news impact of negative news for commodity.Respectively Americanbeef and Chinesebeef are inquired about among Fig. 5.Fig. 5 left-hand component is inquired about for Americanbeef.For rabid ox disease news, system queries is to this news relevant with rabid ox disease (Belinkwith).And rabid ox disease is for Americanbeef influential (Affect), can obtain this negative news by reasoning can be for commodity Americanbeef influential (Maybeaffect), and simultaneity factor further can inquire the factor that affects these commodity and belong to disease(disease class).
Fig. 5 right-hand component is inquired about for Chinesebeef.Detailed process is: for rabid ox disease news, system queries is to this news relevant with rabid ox disease (Belinkwith).And rabid ox disease for Chinesebeef without the impact (NoAffect), can not get the negative news relevant with these commodity by reasoning, do not have potential contact between them, so be the situation of " without factor of influence ", rabid ox disease news example can not affect Chinesebeef.
As shown in Figure 6, whole flow process is the title of commodity of input in the interface externally, import the backstage into, enquiry module meeting loading rule inference machine, a plurality of domain bodies are inquired about, and rule is inquired about the news body at first by inference in native system, inquires about the example of the other influences factor that their connect and infers the commercial examples of this news impact, if then inquire about menu body or product ontology without impact, at last recommendation results outputed on the interface.
Inference mechanism adopts the inference rule of Reasoner inference machine, and is specific as follows:
Inference rule 1:
[rule1:( xhttp://www.semanticweb.org/ontologies/2012/4/market.owl#alwaysbuy y),( yhttp://www.semanticweb.org/ontologies/2012/4/market.owl#alwaysbuy z)->( xhttp://www.semanticweb.org/ontologies/2012/4/market.owl#maybeintereste din z)]
Rule 1 is that the people who buys A often can buy B, and the people who buys B often can buy C, can be interested in C so perhaps buy the people of A.
[rule2:( xhttp://www.semanticweb.org/ontologies/2012/4/market.owl#belinkedwith y),( yhttp://www.semanticweb.org/ontologies/2012/4/market.owl#affect z)->( xhttp://www.semanticweb.org/ontologies/2012/4/market.owl#maybeaffect z)]
Rule 2 is if news item is relevant with a certain problem, and this problem can have influence on a kind of commodity, illustrates that then these commodity and this news is related and can be affected.
The building-block of logic of two rule-based reasoning inquiries:
1, whole querying flow figure as shown in Figure 7, system is inquired about these commodity of Amercianbeef, input in the goods that input is bought: Amercianbeef inquires about and asks recommendation, can obtain the negative news relevant with these commodity by reasoning, and inquire therewith that the factor of influence of these commodity of news association belongs to=http://www.semanticweb.org/ontologies/2012/4/market.owl#disease, the factor that namely affects these commodity belongs to disease(disease class).This is negative effect, thus the output warning prompt, and find relevant news report to read for commodity purchaser.The result is shown as: factor of influence belongs to=＜http://www.semanticweb.org/ontologies/2012/4/market.owl#disease 〉) content of related news report is: it is reported, extensive rabid ox disease occurs in the North America recently, and China is strictly detected North America beef import memory.
2, system is inquired about these commodity of HP-dv7, input in the goods that input is bought: HP-dv7 inquires about and asks recommendation, can obtain the negative news relevant with these commodity by reasoning, and inquire therewith that the factor of influence of these commodity of news association belongs to=http://www.semanticweb.org/ontologies/2012/4/market.owl#quality problem, the factor that namely affects these commodity belongs to qualityproblem(quality problems class).This is negative effect, thus the output warning prompt, and find relevant news report to read for commodity purchaser.
3, system is inquired about these commodity of Chinesebeef, input in the goods that input is bought: Chinesebeef inquires about and asks recommendation, inquire these commodity without factor of influence, carry out next step inquiry, inquire about for the menu body, the vegetable and the way thereof that find beef to act on are returned recommendation.The result shows: without factor of influence, perhaps you can consider this menu " Chinese style goulash ", and way is as follows: " major ingredient: beef/auxiliary material: potato, carrot, capsicum, green onion, ginger, anise, rock sugar, dark soy sauce, cooking wine, oil, salt way: 1, beef is cleaned ... "
4, system is inquired about these commodity of Sony_KDL-46EX520, input in the goods that input is bought: Sony_KDL-46EX520 inquires about and asks recommendation, inquire these commodity without factor of influence, carry out next step inquiry, inquire about for product ontology, carry out reasoning and find the people of frequent these commodity of purchase to return recommendation by interested commodity.The result shows: without factor of influence, you may interested things " god of war ".
In addition can be according to a lot of relevant information of this Ontology Query, but because the present embodiment is the system for commercial product recommending, the contact between some objects is not suitable for showing.
As shown in Figure 5, the inquiry detailed process for Amercianbeef is in the Ontology Query system of the present embodiment:
Can obtain the negative news relevant with these commodity by reasoning, and obtain this negative news and will affect (maybeaffect) Amercianbeef, namely obtain the potential contact of Amercianbeef and rabid ox disease news, so will export prompting " you may have certain health risk by selected commodity at present, please consider as one sees fit to buy! ", the news content that output is relevant simultaneously is for reference.
As shown in Figure 5, the inquiry detailed process for Chinesebeef is and in the Ontology Query system of the present embodiment:
Can not get the negative news relevant with these commodity by reasoning, namely negative news will can not affect (maybeaffect) Chinesebeef, not have potential contact between them, so will export prompting " without factor of influence ".
As shown in Figure 2, the parent of Chinesebeef, Amercianbeef product is beef, then carry out next step inquiry, inquire the high two-stage parent meats class (comprising the subclasses such as beef, chicken, pork) of Chinesebeef, can judge that commodity Chinesebeef belongs to foodstuff, so inquire about for the menu body, the vegetable and the way thereof that find beef to act on are returned recommendation.The similar code of inquiry is as follows:
String classquery=”SELECT y”+“WHERE{eg:”+goods1+”rdf:type x.”+“}”。
Above-mentioned example only is explanation technical conceive of the present invention and characteristics, and its purpose is to allow the people who is familiar with technique can understand content of the present invention and according to this enforcement, can not limit protection scope of the present invention with this.All equivalent transformations that Spirit Essence is done according to the present invention or modification all should be encompassed within protection scope of the present invention.
Claims (6)
1. method of carrying out multi-field Products Show based on the Ontology Query system is characterized in that said method comprising the steps of:
(1) input product information is inquired about in the Ontology Query system, and described Ontology Query system introducing rule-based reasoning machine is retrieved in each ontology library relevant with product;
(2) according to result for retrieval the product information of user's needs is showed user and prompting user Related product with relevant product resource.
2. method according to claim 2 is characterized in that each relevant with product in described method ontology library comprises product quality body, news body, product safety body, product ontology that product is relevant.
3. method according to claim 2, the order that it is characterized in that retrieval in the described method step (1) is to retrieve first the relevant news body of product, and the factor of influence that obtains according to the relevant news body of product judges whether to carry out product safety body and product quality Ontology Query.
4. method according to claim 3 is characterized in that in the described method step (1) that when having factor of influence, the Ontology Query system is inquired about, and inquires about in product ontology in product safety body and product quality body.
5. method according to claim 3 is characterized in that in the described method step (1) that when not having factor of influence, the Ontology Query system is only inquired about in product ontology.
6. method according to claim 3 is characterized in that the rule-based reasoning machine that loads in the described method step (1) is the Reasoner inference machine.
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| CN104599153A (en) * | 2014-08-29 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Commodity recommendation method, commodity recommendation server and commodity recommendation terminal |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104516938A (en) * | 2013-10-07 | 2015-04-15 | 财团法人资讯工业策进会 | Electronic computing device and personalized information recommendation method thereof |
| CN104516938B (en) * | 2013-10-07 | 2017-09-12 | 财团法人资讯工业策进会 | electronic computing device and personalized information recommendation method thereof |
| CN104599153A (en) * | 2014-08-29 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Commodity recommendation method, commodity recommendation server and commodity recommendation terminal |
| CN104599153B (en) * | 2014-08-29 | 2017-02-22 | 腾讯科技（深圳）有限公司 | Commodity recommendation method, commodity recommendation server and commodity recommendation terminal |
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Application publication date: 20130227 |