KR101014429B1 - Modeling method and apparatus for multi-ontology - Google Patents

Abstract

PURPOSE: A multiple ontology modeling method and an apparatus for efficiently reflecting an interference result to the ontology are provided to intuitively confirm whether the modified ontology indicates an interference result about the original ontology. CONSTITUTION: A modified ontology is generated after modifying a constitution element of an original ontology(310). The original ontology and the modified ontology are stored(320). An inputted predetermined query is queried by a plurality of the stored ontology(330). A result about the query is inferred based on the stored ontology(340).

Description

Modeling method and apparatus for multi-ontology

The present invention relates to a multiple ontology modeling method and apparatus, and more particularly, a plurality of users modify an ontology (ontology), a model that abstracts and shares what people think about things, and modified ontology The present invention relates to a collaborative multi ontology modeling method and apparatus for comparing inference results through a query for.

In the traditional view, computer technology has emerged as an aid to get people done quickly and accurately. The development of computer technology has increased the need to move many beings in the real world to objects on the computer in order to allow more work to be done by the computer. In other words, it was necessary to express the understanding of the language in the human head in computer language and make it available to the computer. This allows the machine to better understand and respond appropriately to human needs, such as information retrieval or information processing.

In order to enable smooth communication between people and machines, machines must be able to understand language at a level that people understand. The way a person understands language is usually called conceptualization. As a person experiences each object or event in the world, he grasps the features contained in them and conceptualizes it in language. Similarly, the technology of making a kind of database that corresponds to the concept of human being in computer is called ontology technology. In other words, ontology is a model that abstracts and shares what people think about things. It is a technology that is formalized and explicitly defines the types of concepts and usage constraints.

The technical problem to be solved by the present invention is to solve the problem of relying heavily on the expert of the domain in modeling one ontology, and when the user modified the generated ontology, the modified ontology is compared with the original ontology Determining what kind of discriminatory structure and inference results can be used to overcome difficult limitations.

In addition, another technical problem to be solved by the present invention is to solve the problem that can not effectively reflect the reasoning results in the ontology, and further to query the ontology through a variety of reasoners, overcoming the limitation that can not compare the reasoning results have.

In order to solve the above technical problem, the multiple ontology modeling method according to the present invention comprises the steps of generating one or more modified ontologies by modifying the components of the original ontology; Storing the original ontology and the modified ontology; Receiving a query and querying each of the stored ontology; Inferring a result according to the query based on the stored rules of the ontology; And outputting a difference between inference results corresponding to each of the stored plurality of ontologies.

In order to solve the other technical problem, the multiple ontology modeling method according to the present invention further includes the step of modifying and storing any one of the stored ontologies in consideration of the output difference.

In order to solve the another technical problem, the multiple ontology modeling method according to the present invention further comprises the step of selecting one or more inferencing among a plurality of inference, querying each of the plurality of stored ontologies It is preferable to query the predetermined query through the selected reasoning machine.

Further, the following provides a computer readable recording medium having recorded thereon a program for executing the data structuring method described above on a computer.

In order to solve the above technical problem, the multi-ontology modeling apparatus according to the present invention comprises an input unit for receiving a correction content for the components of the original ontology; A storage unit for storing the ontology modified according to the original ontology and the input correction contents; A query unit which receives a predetermined query and queries each of the stored ontology; A query processing unit for inferring a result according to a query based on the stored rules of the ontology; And an output unit configured to output a difference between inference results corresponding to each of the stored plurality of ontologies.

In order to solve the other technical problem, the input unit of the multiple ontology modeling apparatus according to the present invention further receives a modification to any one of the stored ontologies in consideration of the output difference, the storage unit is further It is desirable to save the modified ontology in accordance with the modifications entered.

In order to solve the another technical problem, the query processing unit of the multiple ontology modeling apparatus according to the present invention is to select one or more reasoners from a plurality of reasoners, and to query the predetermined query through the selected reasoner. desirable.

According to the present invention, in modeling one ontology, various experts in the domain can modify the ontology to generate various ontology so that the collaboration-based ontology can be modeled and the inference results are compared by querying the generated ontology. It is intuitive to see how the ontology can represent the structure and reasoning results of the original ontology.

In addition, the present invention can efficiently reflect the reasoning result in the ontology by modifying the ontology again in consideration of the result of inference before and after the ontology modification, it is also possible to compare the reasoning results through a variety of reasoners.

Prior to describing the embodiments of the present invention, terms and concepts to be used below are briefly described.

As outlined above, in computer science, ontology is a model that expresses the consensus between people about what they see, hear, feel and think about the world in a conceptual and computer-friendly form. domain is defined as a set of formal vocabulary describing the concepts and their relationships. Because ontologies represent knowledge that has been agreed upon once, it is not limited to any individual, but a concept that all members of a group (meaning a specific area) agree with. And because the program must be understandable, formalization is required. In particular, ontology is used as a tool to connect knowledge concepts semantically, and it enables the computer to process and process the concept of human things in the form of a database.

 Ontologies, a set of vocabulary described in formal language, are used for reasoning / inference. In this regard, the semantic web technology has emerged. The semantic web has a relationship between a resource and information about a resource (web document, various files, services, etc.) in a distributed environment such as the current Internet. ) Is a framework and technology that expresses in the form of a machine, that is, an ontology that can be processed by a computer, and let the automated machine process it. In other words, ontology is a tool that can implement semantic web, and it is a tool that can semantically connect knowledge concept.

Elements of ontology can be classified into class, instance, relationship, and property. Explain that a class is usually a name that we attach to things or concepts. "Keyboard", "monitor" and "love" are all classes. Instances, on the other hand, mean the things or concepts that manifest themselves in the actual form of things or events. In other words, "LG Electronics ST-500 Slim Keyboard", "Samsung Syncmaster Wide LCD Monitor" and "Love of Romeo and Juliet" are generally examples. The distinction between classes and instances can vary greatly depending on the application and purpose of use. That is, an object of the same expression may be a class in some cases and an instance in another.

Relationships refer to relationships that exist between classes / instances and can generally be classified into a taxonomic relation and a non-taxonomic relation. A taxonomy relationship is a hierarchical relationship divided into broader concepts and concrete concepts to classify concepts of classes / instances. For example, the "isA" relationship is intended to indicate inclusion between concepts such as "man is an animal." Relationships that are not categorical are called unclassified. For example, "healthy by exercise" is expressed using the "cause" relationship.

An attribute is a concatenation of a class or instance with a specific value to indicate a particular property / propensity of the class or instance. For example, to express "A Samsung Syncmaster Wide LCD monitor is 24 inches," you can define attributes such as hasSize.

In general, relationships and attributes are often not distinguished. Relationships / attributes declared as relationships between real classes / instances such as "isA (human, animal)", "cause", "hasSize (Samsung Syncmaster Wide LCD, 24 inch)" It is also called a relationship / property instance, to distinguish it from the relationship / property defined and defined as "isA", "cause", "hasSize", etc.

Languages commonly used in ontology include resource description framework (RDF), ontology web language (OWL), and semantic web rule language (SWRL). RDF is an evolution of Extensible Markup Language (XML), and consists of triple forms of subjects, objects, and predicates, and simply represents relationships between concepts or instances. In general, you use RDF a lot when calculating general applications that do not require complex constraints. OWL is a language that includes the hierarchy between relationships, logical constraints within relationship instances, and so on. Use when you need precise and logical reasoning. SWRL is used to define rules for inference.

Hereinafter, various embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a view for explaining a process of creating a plurality of ontology by modifying one ontology, a plurality of users, three users 110, 120, 130 to modify the original ontology file x.owl (150) The process is illustrated. In FIG. 1, the vertical axis represents time and indicates that time passes from the top to the bottom. In addition, the modified ontology files 151, 152, 153, 154, and 155 indicate modifications corresponding to the respective users 110, 120, and 130. That is, the x1.owl (151) and x4.owl (154) files mean the ontology file created by the user 1 (110) and the x2.owl (152) and x5.owl (155) files are the user 2 An ontology file modified and generated by 120 may be referred to, and x3.owl 153 may be an ontology file generated and modified by User 3 130.

Here, assume that the original ontology x.owl 150 is an ontology for a vehicle. For example, the first user 110 may create an x1.owl 151 by adding an attribute called “hasType”, which is a classification according to the shape of the vehicle, to the original ontology x.owl 150. Based on the generated x1.owl 151, the user 2 120 may generate an x2.owl 152 including a new attribute “hasManufacturer” relating to the automobile manufacturer. On the other hand, user 3 130 may generate an x3.owl 153 by adding an attribute "hasSize", which is a classification according to the size of the car, to the original ontology x.owl 150. In addition, the user 1 110 may generate an x4.owl 154 by adding an attribute "hasYear", which is an attribute of the production year of the car, based on the x1.owl 151 previously generated by the user. As a result, x4.owl 154 will have both attributes "hasType" and "hasYear". In addition, the user 2 120 may also generate the x5.owl 155 by adding “hasFuelType”, which is an attribute related to the type of fuel used in the vehicle, based on the x2.owl 152 previously generated by the user. . As a result, x5.owl 155 retains all attributes of x.owl 150, x1.owl 151, and x2.owl 152.

As described above, the three users 110, 120, and 130 independently or in cooperation with each other more effectively by generating various modified and supplemented ontologies 151, 152, 153, 154, and 155 from the original ontology 150. Share knowledge and experience with experts in your domain.

Hereinafter, practical technical means for implementing such a collaboration-based ontology modeling method will be described.

FIG. 2 is a block diagram illustrating a multiple ontology modeling apparatus 200 in which a plurality of users generate a plurality of ontology, query a generated multiple ontology, and output a result of inference, according to an embodiment of the present invention. 210, a storage unit 220, a query unit 230, a query processor 240, and an output unit 250.

Basically, the multiple ontology modeling device 200 communicates with and interacts with one or more users 100. To this end, the multiple ontology modeling apparatus 200 may optionally include a communication unit 205. The communicator 205 may be a communication channel or a network interface that physically connects the user 100 and the multiple ontology modeling apparatus 200, but in a broader sense, the communicator 205 may access and interact with the multiple ontology modeling apparatus 200. It may include an interface and a management module for the user 100. That is, the user 100 may input an ontology modification command to the multiple ontology modeling apparatus 200 through the communication unit 205 or may receive an inference result returned in response to a query.

The input unit 210 receives modifications to the components of the original ontology. As described above, the components of an ontology include classes, instances, relationships, and properties. The input unit 210 may include a user interface for receiving a command for instructing to modify the components of the ontology from the user 100. In addition, such an input unit 210 may be implemented as an editor of What You See Is What You Get (WISIWYG) for visual convenience.

The storage unit 220 stores the ontology modified according to the original ontology and the modification contents input through the input unit 210. In particular, the storage unit 220 not only manages the modified final ontology, but also stores all ontology modified again based on the original ontology as well as the original ontology or the modified ontology. If the number of users is N and each of them independently modifies the original ontology, the number of ontology to be stored will be N + 1 at maximum. Accordingly, the storage unit 220 assigns a kind of serial number or version number to the generated ontology so as to systematically manage a plurality of ontology modified by the original ontology or generated / It is desirable to record the date and time of modification.

The storage unit 220 refers to a kind of database or repository for storing ontology accumulated by organizing data and rules related to the corresponding domain, and may store software codes that can be read or stored by a computer system. It may be implemented as a hard disk drive (HDD) or other mass data storage means (mass storage).

The querying unit 230 receives a query from the user 100 and queries each of a plurality of ontology stored in the storage 220. In this case, the query may be a query that inquires about changes in the ontology's components, that is, a structure or property, or a general query for the ontology. The querying unit 230 may be implemented as a processor and a memory required for a task in a typical computer environment, and the computer system may read or store the query to receive the query and to query the ontology stored in the storage unit 220. Process as software code.

The query processor 240 infers the result according to the query based on the rules of the plurality of ontology 225 stored in the storage 220. Here, the plurality of ontology 225 includes both the original ontology and the modified ontology by the user's input. According to the type of query described above in the query unit 230, the former will return a result indicating the structure or property of the ontology through the query, the latter will return a result that can be deduced through the ontology through the query. will be. The query processing unit 240 may also be implemented as a processor and memory required for work in a typical computer environment, and infers a result of inference based on elements such as classes, instances, relationships, or attributes of the ontology stored in the storage unit 220. And process it as software code that the computer system can read or store.

The output unit 250 outputs a difference between inference results corresponding to each of the plurality of ontology 225 stored in the storage unit 220. If the ontology is modified for any kind of query, the result of the inference will be different. In particular, when the components of the ontology are changed, the result of inference can be varied even in the case of a query according to an ordinary query statement (of course, the query can be inferred within the domain of the ontology). That is, the output unit 250 receives different inference results by querying the plurality of ontology 225 for the same query received from the user 100, and compares the inference results to the user 100. Output In addition, the output unit 250 identifies the differences between the inference results, and visually highlights the identified differences.

According to the present embodiment, it is possible for a plurality of users 100 to work together to improve a component of one ontology in abundance, to give the same query to a plurality of ontology 225 generated, and to each ontology. By comparing the inference results corresponding to the field 225, it is possible to accurately determine the performance and characteristics of the modified ontology.

On the other hand, the input unit 210 further receives a modification of any one of the plurality of ontologies 225 stored in the storage unit 220 in consideration of the difference of the inference result output through the output unit 210, and further The modified ontology may be stored in the storage 220 again according to the input correction. This process provides a means for the user 100 to understand the performance and characteristics of each by comparing the inference results of the plurality of ontology 225, and to improve the ontology again based on the result. This iterative process may be repeated as many times as needed by the user. Therefore, through the present embodiment, it is possible to provide an ontology modeling apparatus that continuously develops and enriches its components.

In addition, the query processor 240 may select one or more reasoners 245 from among the plurality of reasoners, and query the query through the selected reasoners. The reasoner 245 is a processing device that infers a new relationship or fact from the ontology 225, and means a semantic reasoner. Such an inference device 245 may include a software portion that is typically implemented as a computer program and a hardware portion of a processor and memory for driving the implemented computer program. Pellet, KAON2 and RacerPro, etc. are known as inferences in the field of ontology or semantic technology, but those skilled in the art can know that various inferences can be used in addition to these inferences. to be.

If more than one reasoner is used, the inference result will be a set of inference results processed for each selected reasoner. In this case, it is preferable that the output unit 250 visually compares a set of inference results processed for each inference unit and outputs the result to the user 100. Through this, the user 100 may be assisted in selecting a more suitable reasoning machine in making a specific query to the ontroll paper 225. Therefore, the query processor 240 may select one or more of the plurality of inferencers 245 by the user's selection.

Meanwhile, the embodiment of the present invention described with reference to FIG. 2 may further include a collection unit 260 and a tagging unit 215.

The collector 260 collects at least one ontology including the original ontology through a network. Multiple ontology can be exposed on the network. If the existing ontology is collected and utilized according to user's needs, a richer ontology model can be built. In other words, if a well-established ontology model can be utilized, the user can save the effort of designing the original ontology without any basic information. Thus, the collection unit 260 collects these ontology. The collection unit 260 may include a software portion implemented as a computer program for performing instructions for collecting ontology through a network, a processing unit for driving the implemented computer program, and a hardware portion of a storage space.

The tagging unit 215 is added to the input unit and adds tag information describing the ontology to the original ontology collected through the collecting unit 260. Here, the tag information refers to additional information such as a definition, an attribute, and a configuration of the original ontology. The tagging unit 215 may add meta data such as bibliographic information of a book to the original ontology. By creating and adding, ontology search is made easier. In addition, the tag information may include classification information of the ontology to help future users classify the ontology. The tagging unit 215 receives a command to add tag information about the ontology through the communication unit 205, and implements a software part implemented as a computer program for performing this, and a processing device and a storage space for driving the implemented computer program. It may include the hardware portion of.

Through the selective configuration as described above, the user of the multiple ontology modeling apparatus 200 collects and edits various ontologies distributed and existing on the network, and attaches and stores additional information about them, thereby generating original and derived ontology. You can manage them more efficiently.

3 is a flowchart illustrating a multiple ontology modeling method in which a plurality of users generate a plurality of ontology, query a generated multiple ontology, and output an inference result according to an embodiment of the present invention. .

In step 310, the components of the original ontology are modified to generate one or more modified ontology. Specifically, this process is performed by receiving modifications to any one of classes, instances, relationships, or attributes of the original ontology from multiple users, and generating modified ontology according to the input modifications. This process corresponds to the contents described above through the input unit 210 of FIG. 2, and a detailed description thereof will be omitted.

In step 320, the original ontology and the modified ontology in step 310 are stored. Thus, the number of ontology to be stored is at least two. If we assume that the number of users is N and modify the original ontology independently, the number of ontology to be stored will be at most N + 1. This process corresponds to the contents described above through the storage unit 220 of FIG. 2, and a detailed description thereof will be omitted.

In step 330, the query is input from the user and the query is queried for each of the plurality of ontology stored in step 320. This process corresponds to the contents previously described through the query unit 230 of FIG. 2, and a detailed description thereof will be omitted.

In operation 340 to 320, the result according to the query is inferred based on the rules of the plurality of ontology stored. This process corresponds to the contents described above through the query processor 240 of FIG. 2, and a detailed description thereof will be omitted.

The difference between the inference results corresponding to each of the plurality of ontologies stored in operation 350 is output. In particular, in outputting the differences between the inference results, it is desirable to identify the differences between the inference results and to visually highlight the identified differences. This process corresponds to the contents described above through the output unit 250 of FIG. 2, and a detailed description thereof will be omitted.

According to this embodiment, it is possible for multiple users to work together to improve the components of an ontology in abundance, to give the same query to the generated multiple ontology, and to infer the inference results corresponding to the respective ontology. By comparison, the performance and characteristics of the modified ontology can be accurately determined.

FIG. 4 is a flowchart illustrating a method of modifying an ontology in consideration of differences between inference results and a method of selecting an inferor for a query in the multiple ontology modeling method according to another embodiment of the present invention. The roll paper modeling method is illustrated by adding steps 325 and 360. The following description will focus on the added steps 325 and 360.

In step 325, one or more reasoners are selected from the plurality of reasoners. As described above, Pellet, KAON2, RacerPro, etc. can be used as the reasoning device in the ontology technology field. Subsequently, in step 330, the query statement is queried to the ontology stored in step 320 through the reasoning machine selected in step 325. As a result, the inference result returned in step 340 corresponds to a set of inference results processed for each inference machine selected in step 325.

According to this embodiment, it is possible to infer a variety of results from the ontology that is the query target through the use of a variety of reasoners, it is possible to provide the judgment data to allow the user to select a more suitable reasoner.

On the other hand, step 360 modifies and stores any one of the stored ontologies in consideration of the difference between the inference results output through step 350. That is, the user can model the improved ontology by comparing the inference results and reflecting additional modifications to the ontology. The improved ontology can be queried again, and the ontology can be refined through this iterative process.

5A to 5C are views illustrating a method and a result of confirming changes in an original ontology and a modified ontology in a multiple ontology modeling apparatus according to an embodiment of the present invention, and are present in a domain for food. It shows an ontology that abstracts the concepts.

5A illustrates a screen in which an original ontology of food is displayed through a user interface of the multiple ontology modeling apparatus described above. The top left of the screen shows the original ontology with Localname 'foodOntology'. The bottom left of the screen shows the version number and creation date of the selected original ontology. 5A shows the original ontology, so the ontology version corresponds to '1'. The right side of the screen shows the class structure and properties of the selected source ontology. For now, assume that 'fruit' is selected among the classes, and the corresponding properties are displayed in the lower right corner. In this case, since the attribute of 'fruit' is not defined in the original ontology of FIG. 5A, no content is displayed in the attribute information window at the lower right corner.

Now, the user looks at the ontology generated by modifying the original ontology of Figure 5a. FIG. 5B is a diagram illustrating the ontology stored as a result of the modification, and two stored ontology are displayed at the lower left of the screen. That is, the original ontology corresponding to version '1' and the modified ontology corresponding to version '2' are stored. Naturally, it can be seen that the creation date of the modified ontology of version '2' is later than the original ontology. In the upper right corner of the screen, the class structure of the modified ontology of version '2' is shown. You can see the newly added subclasses of 'fruit' as 'apple', 'banana' and 'peach'. Also, you can see that 'hasName', 'hasScent', and 'hasTaste' are added as properties of 'fruit' at the bottom right of the screen.

FIG. 5C is a diagram illustrating a query result returned by a multiple ontology modeling apparatus inquiring that an ontology component is changed through the above method. In the present embodiment, SPARQL (SPARQL Protocol and RDF) is used as a language for querying. Query Language) is used. SPARQL, as its name suggests, is a query language proposed for applications to perform sophisticated queries against RDF databases in semantic web environments. Basically, SPARQL is similar to SQL, the query language for relational databases, specifying the SELECT clause for the part of the RDF that you want to see as a search result, enumerating the target RDF in the FROM clause, and the WHERE clause. The part corresponding to the condition is described.

First, SPARQL for querying the structure of a class is inputted to Query SPARQL Representation of the screen break of FIG. 5C as shown in the following Query 1.

[Question 1]

SELECT? Class? SuperClass WHERE {? Class rdfs: subClassOf? SuperClass. }

Next, query 1 is selected from one of the various reasoners. In FIG. 5C, it can be seen that 'pellet' is selected as an inferencing machine. If you execute 'Query 1' by selecting 'Run' button, the query processing result is displayed in Query Execution Results at the bottom of the screen. In FIG. 5C, it can be seen that 'Banana' is a subclass of the class 'Fruit'.

Meanwhile, to check the property of a class, the following query 2 can be used.

[Question 2]

SELECT? Class? Properties WHERE {? Class rdfs: subPropertyOf? Properties. }

If query 2 is executed under the assumption that the ontology of version '2' of FIG. 5B is stored, 'hasName', 'hasScent', and 'hasTaste' may exist as attributes of 'fruit'. .

By utilizing the multiple ontology modeling apparatus according to the above embodiments, it is possible for a user to more easily query modifications to the components of the ontology, and visualize and provide the query results to the user.

 Meanwhile, the present invention can be embodied as computer readable codes on a computer readable recording medium. The computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer system is stored.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like, which may also be implemented in the form of carrier waves (for example, transmission over the Internet). Include. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. In addition, functional programs, codes, and code segments for implementing the present invention can be easily deduced by programmers skilled in the art to which the present invention belongs.

The present invention has been described above with reference to various embodiments thereof. Those skilled in the art will understand that the present invention can be implemented in a modified form without departing from the essential features of the present invention. Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope will be construed as being included in the present invention.

FIG. 1 is a diagram illustrating a process of generating a plurality of ontology by modifying one ontology by a plurality of users.

FIG. 2 is a block diagram illustrating a multiple ontology modeling apparatus in which a plurality of users generate a plurality of ontology, query a generated multiple ontology, and output an inference result.

3 is a flowchart illustrating a multiple ontology modeling method in which a plurality of users generate a plurality of ontology, query a generated multiple ontology, and output a result of inference according to an embodiment of the present invention.

FIG. 4 is a flowchart illustrating a method of modifying an ontology in consideration of differences between inference results and selecting a reasoner for a query in a multiple ontology modeling method according to another exemplary embodiment of the present invention.

5A to 5C are diagrams illustrating a method and a result of checking changes in an original ontology and a modified ontology in a multiple ontology modeling apparatus according to an exemplary embodiment of the present invention.

Description of the main parts of the drawing

100, 110, 120, 130: user

200: multiple ontology modeling device

205: communication unit

210: input unit 215: tagging unit

220: storage unit 225: ontology

230: query unit 240: query processing unit

245: reasoning machine

250: output unit 260: collector

Claims (16)

Modifying components of the original ontology to generate one or more modified ontology; Storing the original ontology and the modified ontology; Receiving a query and querying each of the stored ontology; Inferring a result according to the query based on the stored rules of the ontology; And And outputting a difference between inference results corresponding to each of the stored plurality of ontology. The method of claim 1, And modifying and storing any one of the stored plurality of ontologies in consideration of the output difference. The method of claim 1, Selecting one or more reasoners from among the plurality of reasoners, The querying of each of the stored plurality of ontologies comprises querying the predetermined query through the selected inference unit. The method of claim 3, wherein And the inference result is a set of inference results processed for each of the selected inference machines. The method of claim 1, The step of outputting the difference between the inference results, the multiple ontology modeling method characterized in that for identifying the difference between the inference results, and visually highlighting the identified difference. The method of claim 1, Generating the at least one modified ontology, Receiving modifications to any one of a class, an instance, a relationship, or an attribute of the original ontology from a plurality of users; And The ontology modeling method comprising the step of generating a modified ontology according to the input correction. The method of claim 1, And collecting at least one or more ontology including the original ontology through a network. The method of claim 7, wherein And adding tag information describing the ontology to the collected original ontology. A non-transitory computer-readable recording medium having recorded thereon a program for executing the method of claim 1. An input unit for receiving modifications to the components of the original ontology; A storage unit for storing the ontology modified according to the original ontology and the input correction contents; A query unit which receives a predetermined query and queries each of the stored ontology; A query processing unit for inferring a result according to a query based on the stored rules of the ontology; And And an output unit configured to output a difference between inference results corresponding to each of the stored plurality of ontology. 11. The method of claim 10, The input unit further receives a modification of any one of the stored ontologies in consideration of the output difference, And the storage unit stores the ontology modified according to the further input correction. 11. The method of claim 10, And the query processing unit selects one or more reasoners from a plurality of reasoners, and queries the predetermined query statement through the selected reasoners. 13. The method of claim 12, And the inference result is a set of inference results processed for each of the selected inference machines. 11. The method of claim 10, And the output unit identifies differences between the inference results and visually highlights the identified differences. 11. The method of claim 10, Multiple ontology modeling apparatus further comprising a collector for collecting at least one or more ontology including the original ontology over a network. The method of claim 15, The input unit further comprises a tagging unit for adding the tag information for describing the ontology to the collected original ontology.

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