JP4580352B2 - Ontology approximation device, ontology approximation method, and ontology approximation program - Google Patents

Description

The invention ontology of similarity measuring device, a similarity measurement program of similarity measuring method and ontology ontology, in particular, with reference to the blog entry, a personal ontology interest information of individuals has been the concept hierarchical automatically It is suitable to apply to a method for efficiently measuring the degree of approximation between personal ontologies.

In the conventional method for calculating the degree of approximation between ontology, as disclosed in Non-Patent Document 1, an ontology constructed in a completely distributed environment is targeted. For this reason, as a pre-process for calculating the degree of approximation between ontology, a correspondence relationship between classes of ontology is derived.
FIG. 5 is a diagram illustrating a configuration example of an ontology constructed in a conventional completely distributed environment.
In FIG. 5, the ontology OA has a class called “lock”. The class “Rock” has a child class “US”, the child class “US” has a child class “Indie”, and the child class “Indies” has a “Grunge” class. There is a child class. Further, it is assumed that instances “nirvana”, “Foo Fighters”, and “super chunk” exist in the child class “grunge”.

On the other hand, the ontology OB has a class called “lock”. The class “Rock” has child classes “America” and “United Kingdom”, the child class “ America” has a child class “indies”, and the child class “indies” Assume that instances “nirvana” and “Stone temple pilots” exist.

Furthermore, the child class “UK” has a child class “Alternative”, and the child class “Alternative” has a child class “Scotland”. It is assumed that an instance of “tenage” exists in “Scotland”.
When the degree of approximation between the ontology OA and OB is measured, mapping between classes included in the ontology OA and OB is performed. Here, when mapping between classes included in the ontology OA and OB is performed, the degree of approximation can be measured for all combinations between classes constituting the ontology OA and OB.

  That is, as shown in FIG. 6 and FIG. 7, mapping between the ontology OA and OB can be performed by calculating the approximate degree between all classes included in each ontology OA and OB. . As a method of measuring the degree of approximation, it can be calculated based on the degree of approximation of class characteristics such as name attributes, various properties, and instance sets of a class.

FIG. 8 is a diagram illustrating an example of a mapping result between the classes of FIG.
In FIG. 8, as a result of mapping between ontology OA and OB, the ontology OA “lock” class and ontology OB “lock” class are mapped, and ontology OA “US” class and ontology OB “USA” are mapped. ”Is mapped, ontology OA“ indie ”class is mapped to ontology OB“ indies ”class, ontology OA“ grunge ”class and ontology OB“ indies ”class is mapped It shall be assumed.

  When mapping between the classes of ontology OA and OB is performed, the degree of approximation between the class sets composed of the adjacent classes of the class having a high degree of approximation is measured. As a method for measuring the degree of approximation between class sets, for example, a Jaccard coefficient can be used. With this Jaccard coefficient, the degree of approximation between the class sets X and Y can be obtained by (X∩Y) / (X∪Y).

FIG. 9 is a diagram illustrating an example of the degree of approximation measurement result between the ontology in FIG.
In FIG. 9, it is assumed that the mapping result of FIG. 8 is obtained between the classes of ontology OA and OB of FIG. In this case, in ontology OA, a class set G11 consisting of a class called "lock" and a class called "US", a class called "lock", a class set G12 consisting of a class called "US" and a class called "indie", "indie" And a class set G13 including the class “grunge” is obtained.

  On the other hand, in ontology OB, a class set G21 consisting of a class "Rock", a class "America" and a class "UK", a class set "Class" "Rock", a class "America" and a class "Indies" G22, a class set G23 consisting of a class “America” and a class “indies” is obtained.

  Between the class sets G11 and G21, the class “lock” in the class set G11 and the class “lock” in the class set G21 are in a correspondence relationship. Since the class “US” and the class “USA” in the class set G21 are in a corresponding relationship, they are regarded as one member, and the class set G21 includes a class “UK”. The total number of members G11 and G21 (G11∪G21) is 3.

  On the other hand, the class “lock” in the class set G11 and the class “lock” in the class set G21 have a correspondence relationship, and the class “US” in the class set G11 and the class “America” in the class set G21 correspond to each other. Since there is no class corresponding to “UK” in the class set G21 in the class set G11, the number of members commonly included between the class sets G11 and G21 (G11∩G21) is two. As a result, the Jaccard coefficient between the class sets G11 and G21 is 2/3.

Similarly, the Jaccard coefficient between the class sets G12 and G22 is 2/4, and the Jaccard coefficient between the class sets G13 and G23 is 1/3.
When the Jaccard coefficients between the ontology OA and OB class sets are found, the Jaccard coefficients between these class sets are added together. Then, by dividing the result obtained by adding the Jaccard coefficients by the number of classes of the source ontology OA, the degree of approximation of the ontology OB viewed from the ontology OA can be measured. For example, in FIG. 9, since the number of classes of the source ontology OA is 4, the degree of approximation of the ontology OB viewed from the ontology OA is (2/3 + 2/4 + 1/3) / 4 = 3/8.

FIG. 10 is a diagram illustrating a comparative example of the degree-of-approximation measurement results for a plurality of conventional ontology.
In FIG. 10, it is assumed that the ontology OC has no child class below the class “UK” of the ontology OB. In the ontology OC, a class set G31 composed of a class “Rock” and a class “America”, a class set G32 composed of a class “Lock”, a class “America” and a class “indies”, “America” A class set G33 including the class and the class “indies” is obtained.

In this case, the Jaccard coefficient between the class sets G11 and G31 is 2/2, the Jaccard coefficient between the class sets G12 and G32 is 2/4, and the Jaccard coefficient between the class sets G13 and G33 is 1/3. As a result, the degree of approximation of ontology OC viewed from ontology OA is (2/2 + 2/4 + 1/3) / 4 = 11/24.
Non-Patent Document 2 discloses a method for realizing semi-automatic mapping between classes constituting a plurality of ontology.

Maedche, A .; and Staab, S .; : Measuring Similarity between Ontologies, In Technical Report, E0448, University of Karlsruhe (2001) Makoto Nakajo, Yu Miyoshi, Yasuyuki Kimura: “Proposal and Evaluation of Message Mapping Method Based on Semantic Information for Flexible System Cooperation”, Database Society of Japan Letters, Vol. 4, no. 1, pp. 37-40 (2005)

However, in the method disclosed in Non-Patent Document 1, in order to calculate the degree of approximation between ontology, it is necessary to determine the correspondence (class mapping) between the classes possessed by the ontology. When the number increases, there is a problem that the calculation amount becomes enormous.
Further, according to the method disclosed in Non-Patent Document 1, the ontology OB and OC approximated from the source ontology OA shown in FIG. 10 are similar to the ontology OB although the ontology OB has more knowledge than the ontology OC. The ontology OC is larger than the ontology OC, and there is a problem that the ontology having a large amount of knowledge cannot be properly extracted.

  Furthermore, in the method disclosed in Non-Patent Document 1, an instance set is used only for identifying classes corresponding to different ontologies, and the degree of approximation between ontologies is calculated while considering the co-occurrence of instances in different ontologies. Since the calculation cannot be performed, there is a problem that it is not always possible to appropriately extract an ontology that suits individual tastes.

  For example, in FIG. 9, in ontology OA and OB, an instance “nirvana” under the class “grunge” and an instance “nirvana” under the class “indies” co-occur. Co-occurrence is not considered, and only the correspondence between the class “grunge” and the class “indies” is considered. For this reason, even when the ontology OA and OB instances do not match at all, the correspondence between the classes may be identified only from the approximation of the name attribute. If the class hierarchy is close, the approximation between the ontology OA and OB is high. There was a problem of being judged. This is a problem when it is desired to finally handle an instance as concrete information.

In addition, the method disclosed in Non-Patent Document 2 measures the correspondence between classes between ontology constructed in a completely distributed environment. There was a problem that the degree of approximation was not calculated.
Therefore, an object of the present invention is to calculate the degree of approximation between ontology while suppressing the amount of calculation, and to calculate the degree of ontology that can appropriately extract ontology with abundant knowledge amount that suits individual tastes. measuring device is to provide a similarity measure programs of similarity measuring method and ontology ontology.

In order to solve the above-described problem, the ontology approximation degree measuring device according to claim 1 includes a word extracting unit that extracts words included in the interest information stored in the storage area and constructed for each individual, and a storage A classifier applying means for extracting a class or an instance including a word extracted by the word extracting means from a template ontology formed by conceptually stratified words stored in a region, and the extraction for each individual A personal ontology extracting means for extracting a hierarchical structure including the created classes or instances and their higher classes as a personal ontology from the template ontology and storing it in a storage area, and a connection between the classes forming the hierarchical structure in the personal ontology For the topology representing the form, two different stored in the storage area A degree-of-approximation measuring means for measuring the degree of approximation between the topologies of the sonality ontology, wherein the degree of approximation measuring means defines a set of child classes having one class as a parent class as a child class set; A child class set whose parent class is a common class including the same word in the personal ontology is X and Y, and a child class set whose parent class is a class including the same word as the common class in the template ontology is Z. Then, a value obtained by dividing the number of members of the class belonging to the product set of the child class sets X and Y by the number of members of the class belonging to the child class set Z is set as the degree of approximation between the child class sets X and Y. For all child class sets whose parent class is a common class containing the same word in two different personal ontology Means for calculating a similarity score, characterized in that it comprises means for calculating the sum of the degree of approximation between all child class set of calculation as the approximation degree between the topology of the personal ontology, the.

  As a result, a personal ontology can be constructed from the same template ontology. Between these personal ontologies, regularity such as classes constituting the personal ontology, instances constituting the class, topology that is a connection form of a plurality of classes, and the like. Can be maintained. For this reason, the degree of approximation between personal ontologies is calculated without sequentially calculating the degree of approximation between all classes included in the personal ontology or sequentially examining the correspondence between classes adjacent to the corresponding class. And the amount of calculation of the degree of approximation between personal ontology can be suppressed.

Further , the degree of topology approximation between personal ontology can be measured based on the number of members of the class set of the template ontology. For this reason, even when the number of members of the class set of personal ontology increases, it is possible to prevent the degree of topology approximation between personal ontologies from decreasing, and a personal with a large amount of knowledge in the sense that there are abundant classes. The degree of approximation with ontology can be increased.

Also, similarity measurement apparatus ontology according to the second aspect, the word extraction means for extracting words included in and stored in the storage area interesting information constructed for each individual and previously set stored in the storage area Classifier application means for extracting a class or instance including a word extracted by the word extraction means from a template ontology in which the extracted words are conceptually hierarchized, and for each individual, the extracted class or instance and the A personal ontology extraction means for extracting a hierarchical structure including higher classes from the template ontology as a personal ontology and storing it in a storage area; and between two common classes containing the same word in two different personal ontology stored in the storage area An approximation degree measuring means for measuring the degree of approximation of the approximation degree measuring means, A set of instances belonging to one class is referred to as an instance set, an instance set belonging to a common class including the same word in two different personal ontology is set to x and y, and the same word as the common class is included in the template ontology When the instance set belonging to the class is z, a value obtained by dividing the number of members of the instance belonging to the product set of the instance sets x and y by the number of members of the instance belonging to the instance set z is between the instance sets x and y. A means for calculating the degree of approximation between the instance sets for all the instance sets belonging to a common class including the same word in two different personal ontologies, and a sum of the degrees of approximation between all the calculated instance sets Personal on Characterized in that it comprises means for calculating a degree of approximation between LOGIS class, the.

  As a result, a personal ontology can be constructed from the same template ontology. Between these personal ontologies, regularity such as classes constituting the personal ontology, instances constituting the class, topology that is a connection form of a plurality of classes, and the like. In addition, the co-occurrence degree between the instance sets can be directly used to measure the degree of approximation between personal ontology. For this reason, the degree of approximation between personal ontologies is calculated without sequentially calculating the degree of approximation between all classes included in the personal ontology or sequentially examining the correspondence between classes adjacent to the corresponding class. It is possible to suppress the amount of calculation of the degree of approximation between personal ontology, and it is possible to prevent the degree of approximation between completely different instances of personal ontology from becoming high, which is suitable for personal preference. Ontologies can be extracted properly.

Also, based on the number of members of the instances set chicks shaped ontology, it is possible to measure the degree of approximation classes between personal ontology. For this reason, even when the number of members of the instance set of personal ontology increases, it is possible to prevent the closeness of the class between personal ontologies from decreasing, and a personal with a large amount of knowledge in the sense that there are abundant instances. The degree of approximation with ontology can be increased.

Further, the ontology approximation degree measuring method according to claim 3 is an ontology approximation degree measuring method executed by an approximation degree measuring apparatus that measures the degree of approximation between two different personal ontology, and is stored in a storage area. by applying the morphological analysis on a blog entry, the step of selecting a template ontology extracting words included in the blog entry, word which is stored in the storage area and pre-set formed by the concept hierarchical If, from the template ontology, the steps to extract the class or instance including words extracted from the blog entry, for each individual, a hierarchical structure that includes a class of the extracted class or instance and their upper stored in the extracted memory area from the template ontology as a personal ontology And step for topology representing the connection form between classes forming the hierarchical structure in the personal ontology, the step of measuring the degree of approximation S _T between the topology of the two personal ontology different stored in the storage area, a step of measuring the degree of approximation S _C between common class that contains the same words in the two personal ontology different stored in the storage area, the degree of approximation between two different personal ontology S _{0 (AB),} the topology And measuring the approximation S ₀ (AB) from S ₀ (AB) = S _T + f (S _C ) , where f (X) is an evaluation function corresponding to the importance with respect to the class , the step of measuring the degree of approximation S _T between the topology child class the set of parent class and child class one class A set of child classes having a common class including the same word in two different personal ontology as the parent class as X and Y, and a class including the same word as the common class in the template ontology as a parent class When the child class set to be performed is Z, a value obtained by dividing the number of members of the class belonging to the product set of the child class sets X and Y by the number of members of the class belonging to the child class set Z is the child class set X and Y Calculating a degree of approximation between the child class sets for all child class sets having a common class including the same word in two different personal ontologies as a parent class, and all the calculated child classes Yes a step of computing the sum of the similarity between the set as the degree of approximation S _T between the topology of the personal ontology, the The step of measuring the degree of approximation S _C between the classes, the set of instances that belong to one class and instance set different in the two personal ontology instances set belonging to a common class that contains the same words as x and y And the instance set belonging to the class including the same word as the common class in the template ontology is z, the number of instances belonging to the product set of the instance set x and y is the number of instances belonging to the instance set z. A value obtained by dividing the number of members as an approximation between the instance sets x and y, and calculating an approximation between the instance sets for all instance sets belonging to a common class including the same word in two different personal ontologies; , All calculated And having the steps of: calculating a degree of approximation S _C between classes of personal ontology sum of similarity between the instances set.
This makes it possible to build a personal ontology while inheriting the class characteristics of the template ontology, and can directly use the co-occurrence between instance sets to measure the degree of approximation between personal ontology. It is possible to appropriately extract a personal ontology that suits individual tastes while suppressing the above.

Also, similarity measurement program ontology of claim 4, wherein, by applying the morphological analysis on blog entries stored in the storage area, extracting words included in the blog entry, storage area a method comprising the steps of stored words and preset to select the model ontology formed by conceptual layered, from the template ontology, to extract the class or instance including words extracted from the blog entry, each For each individual, a step of extracting a hierarchical structure including the extracted class or instance and higher classes thereof from the template ontology as a personal ontology and storing it in a storage area ; and a class forming the hierarchical structure in the personal ontology The topology representing the connection between the two Approximation between common classes comprising the steps of measuring the degree of approximation S _T, the same words in the two personal ontology different stored in the storage area between the topology of the two personal ontology different stored in the storage area a step of measuring the degree S _C, different degrees of approximation between the two personal ontology S _{0 (AB),} when the evaluation function according to the importance for the topology and class was f (X), the degree of approximation S ₀ (AB), a step of measuring from S ₀ (AB) = S _T + f (S _C ), an ontology approximation degree measurement program that causes a computer to execute the measurement, and measuring the approximation degree S _T between the topologies In this step, a set of child classes having one class as a parent class is defined as a child class set, and two different personal ontologies are used. A child class set whose parent class is a common class including the same word is X and Y, and a child class set whose parent class is a class including the same word as the common class in the template ontology is Z. The value obtained by dividing the number of members of the class belonging to the product set of the child class sets X and Y by the number of members of the class belonging to the child class set Z is an approximation between the child class sets X and Y. Calculating a degree of approximation between the child class sets for all child class sets whose parent class is a common class including the same word in one personal ontology, and a sum of the degrees of approximation between all the calculated child class sets. Calculating the approximation S _T between the topologies of the personal ontology, and measuring the approximation S _C between the classes Is a set of instances belonging to one class as an instance set, an instance set belonging to a common class including the same word in two different personal ontology is x and y, and the same word as the common class in the template ontology Where z is the instance set belonging to the class including the instance set x and y, the value obtained by dividing the number of instances belonging to the product set of the instance set x and y by the number of instances belonging to the instance set z A degree of approximation between the instance sets for all of the instance sets belonging to a common class including the same word in two different personal ontologies; Sum to persona A step of computing a degree of approximation S _C between ontology classes, that have the features.

Accordingly, by executing the interest information generation program into the computer, while inheriting the class characteristics of model ontology it becomes possible to construct a personal ontology, the co-occurrence degree between instances set between the personal ontology It can be used directly for the measurement of the degree of approximation, and it is possible to appropriately extract a personal ontology that suits individual preferences while suppressing the amount of calculation.

  As described above, according to the present invention, it becomes possible to accurately search for information suitable for one's preference, and to widely distribute information suitable for one's preference on the Internet, A community that suits individual preferences can be formed.

Hereinafter, an ontology approximation measuring apparatus and method according to an embodiment of the present invention will be described with reference to the drawings.
FIG. 1 is a block diagram showing a schematic configuration of a system to which an ontology approximation measuring apparatus according to an embodiment of the present invention is applied.
In FIG. 1, terminals 2 to 4 and a server 5 are connected via a communication network 1. As the communication network 1, for example, a public communication network that performs IP communication can be used, and the Internet may be used. Further, it may be a private communication network between companies or a public communication network, but it is like an IP-VPN (Internet Protocol-Virtual Private Network) that can provide dedicated communication with high reliability and security. A simple net may be used.

  Further, the terminals 2 to 4 may be a notebook personal computer or a desktop personal computer, and may be a mobile phone terminal or a PDA (Personal Data Assistant). The server 5 can be installed on a blog provider or ISP (Information Service Provider).

  Here, the server 5 is provided with blog sites 7 to 9 corresponding to the terminals 2 to 4 respectively, and the blog entries 7a to 7n, 8a to 8n, and 9a to 9n are held in the blog sites 7 to 9, respectively. Has been. The blog entries 7a to 7n, 8a to 8n, and 9a to 9n represent minimum units of articles in the blog, and can be provided for each day. The server 5 holds a template ontology 6, and the template ontology 6 is provided with a personal ontology template in which personal interest information is conceptually hierarchized.

  The template ontology 6 can be arbitrarily created on the blog provider side. For example, if the blog provider wants the users of the terminals 2 to 4 to construct a personal ontology related to music, the template ontology 6 related to music may be constructed. Here, in order to express the interests of the users of the terminals 2 to 4 in detail, it is preferable to construct a template ontology 6 that is subdivided as much as possible and has high coverage. The entity of the template ontology 6 is a representation of a class / instance tree on the DB, such as a text file described in an XML language such as the ontology description language OWL. In order to simplify the organization of information, instances may be classified only into the lowest class.

  Furthermore, the server 5 applies words that appear frequently in the blog entries 7a to 7n, 8a to 8n, and 9a to 9n by applying morphological analysis to the blog entries 7a to 7n, 8a to 8n, and 9a to 9n, respectively. In the frequent word extraction means 5a to be extracted, the classifier application means 5b and the classifier application means 5b to extract from the template ontology 6 a class or instance including words that frequently appear in the blog entries 7a to 7n, 8a to 8n, and 9a to 9n. The personal ontology extracting means 5c for extracting the extracted class or instance and all the classes above them as a personal ontology from the template ontology 6 and the class ID or instance ID allocated from the template ontology 6 are held in the personal ontology. Based on the judgment result Te, similarity measurement unit 5d that measures the degree of approximation topology or classes between personal ontology is provided.

Here, the degree-of-approximation measuring means 5d uses the degree of co-occurrence between the class sets forming the topology of the personal ontology extracted by the personal ontology extracting means 5c when measuring the degree of approximation of the topology between personal ontology. Can do. Further, when measuring the degree of approximation of classes between personal ontology, the degree of approximation measuring means 5d can use the degree of co-occurrence between instance sets included in the personal ontology extracted by the personal ontology extracting means 5c. The co-occurrence degree is a ratio of a product set of members included in the set.
The frequent word extraction means 5a applies morphological analysis to the blog entries 7a to 7n, 8a to 8n, and 9a to 9n, respectively. Then, morphemes that frequently appear in a plurality of blog entries 7a to 7n, 8a to 8n, and 9a to 9n possessed by the same user are extracted.

  Next, the classifier applying unit 5b applies each morpheme frequently appearing in the blog entries 7a to 7n, 8a to 8n, and 9a to 9n to the template ontology 6, and a character string that matches the class or instance in the template ontology 6 is applied. Find out if there is. When there is a character string that matches the class or instance in the template ontology 6, the personal ontology extracting means 5c determines the direct descendant classes and instances from the root class of the template ontology 6 to the class or instance. Extract as

  Furthermore, the degree-of-approximation measuring means 5d measures the degree of topology approximation between personal ontology based on the degree of co-occurrence between class sets forming the topology of the personal ontology extracted by the personal ontology extracting means 5c. For example, the degree-of-approximation measuring unit 5d measures the degree of topology approximation between personal ontology based on the value obtained by dividing the number of members of the product set between the class sets of personal ontology by the number of members of the class set of template ontology 6. can do.

  Further, the degree-of-approximation measuring means 5d measures the degree of approximation of classes between personal ontology based on the degree of co-occurrence between instance sets included in the personal ontology extracted by the personal ontology extracting means 5c. For example, the degree-of-approximation measuring means 5d measures the degree of class approximation between personal ontology based on the value obtained by dividing the number of members of the intersection set between instances of personal ontology by the number of members of the instance set of template ontology 6. can do. Then, the degree-of-approximation measuring means 5d can measure the degree of approximation between personal ontology by comprehensively judging the degree of approximation of topology between personal ontology and the degree of approximation of class between personal ontology.

  Thus, by comparing the words included in each person's interest information on the template ontology 6, regularity such as classes constituting the personal ontology, instances constituting the classes, topology being a connection form of a plurality of classes, and the like can be obtained. It is possible to generate a personal ontology that reflects the interests of the individual while maintaining it, and by checking whether the personal ontology has the class or instance of the template ontology 6, the topology or class between the personal ontology The degree of approximation can be measured.

  For this reason, it is possible to generate personal ontology while suppressing the cost of creation, improve the accuracy of information retrieval, and class attributes such as name attributes, various properties, and instance sets. The degree of approximation between ontology can be calculated without considering the characteristics, and the amount of calculation of the degree of approximation between ontology can be suppressed.

  Note that the template ontology 6 may be manually created by desk work and stored in the server 5, or the personal ontology extracted by the personal ontology extracting means 5c may be merged with the existing template ontology 6. You may create it. Furthermore, a class or instance that the user is interested in may be added to the personal ontology extracted from the template ontology 6, or a class or instance that the user is not interested in may be deleted.

Also, frequent word extraction unit 5a, classifier applying means 5b, personal ontology extractor 5c and similarity measuring means 5d is possible to execute the program instructions are written to perform the processing performed by these means to the computer Can be realized.
Then, by storing this program in a storage medium such as a CD-ROM, by mounting the storage medium in the server 5 of the computer, to install the program on a computer, frequently appearing word extracting unit 5a, classifier The processing performed by the applying unit 5b, the personal ontology extracting unit 5c, and the approximation degree measuring unit 5d can be realized. Furthermore, the program, by downloading via the communication network 1, it is possible to the program easily spread.

Also, the case of executing the frequent word extracting unit 5a, classifier application means 5b, the program instruction is described to perform the processing performed by the personal ontology extractor 5c and similarity measurement unit 5d to a computer, a stand-alone computer It may be executed, or may be distributed to a plurality of computers connected to the network.

FIG. 2 is a diagram illustrating a method for measuring the degree of approximation between personal ontology according to an embodiment of the present invention. In the following description, when the degree of approximation between an ontology and another ontology is measured, the former is called a source ontology and the latter is called a target ontology.
In FIG. 2, classes “b1”, “b2”, and “b3” exist immediately below the class “a1” of the template ontology OH, and “c1” and “c3” immediately below the class “b1”. It is assumed that a class called “c2” exists and classes “d1” and “d2” exist immediately below the class called “c1”. Also, it is assumed that classes “c3” and “c4” exist immediately below the class “b2”, and a class “c5” exists immediately below the class “b3”.

  The class “d1” includes instances “j” and “k”, the class “d2” includes the instance “l”, and the class “c2” includes “m”. "B", the class "b2" has an instance "n", and the class "c3" has "a", "e", "c", "f", "b" ”,“ D ”, and“ g ”exist, and the class“ c4 ”includes instances“ p ”,“ g ”,“ j ”, and“ h ”.

Then, the words that frequently appear in each user's blog entry are extracted, and the classes or instances including the words and all the classes above them are extracted from the template ontology OH, thereby creating personal ontologies OA and OB. Shall be.
Here, the classes “b1” and “b2” exist immediately below the class “a1” of the personal ontology OA, and the classes “c1” and “c2” exist immediately below the class “b1”. It is assumed that a class “d1” exists immediately below the class “c1”, and a class “c3” and “c4” exist immediately below the class “b2”. , The class “d1” includes instances “j” and “k”, the class “c2” includes the instance “m”, and the class “c3” includes “a”. , “C”, “b”, and “d” exist, and the class “c4” includes instances “q” and “h”.

  Also, in the personal ontology OB, there are “b1”, “b2”, and “b3” classes directly under the “a1” class, and there is a “c1” class directly under the “b1” class. Assume that a class “d1” exists immediately below the class “c1”, and a class “c3” and “c4” exist immediately below the class “b2”. The class “d2” has an instance “l”, the class “b2” has an instance “n”, and the class “c3” has “a” and “c”. ”,“ E ”, and“ f ”exist, and“ c4 ”class includes“ p ”and“ j ”.

  Then, a common class excluding a terminal class is analyzed between the template ontology OH and the personal ontology OA and OB, and a topology composed of parent and child classes with the common class as a parent class is extracted. In the example of FIG. 2, the end classes are “d1”, “d2”, “c3”, and “c4”. As a result, a child class set G1 having the class “a1” as the parent class, a child class set G2 having the class “b1” as the parent class, and the class “c1” being the parent class between the template ontology OH and the personal ontologies OA and OB. Child class set G3, child class set G4 having class “b2” as a parent class, and child class set G5 having class “b3” as a parent class can be extracted.

The analysis of the common class only needs to confirm whether the same class ID exists between the template ontology OH and the personal ontology OA, OB. For this reason, it is not necessary to measure the degree of approximation of class name attributes, instance set properties, etc., and the amount of calculation can be reduced while maintaining the correspondence between classes accurately.
Next, the degree of approximation between the child class sets X and Y forming each topology between the personal ontology OA and OB is calculated with depth priority. Here, if the set of child classes of the classes constituting the template ontology OH is Z, the degree of approximation between the child class sets X and Y can be obtained by | X∩Y | / | Z |. By adding the approximation degree between the child class set for each topology, it is possible to measure the degree of approximation S _T personal ontology OA, between OB topology.

  For example, in the child class set G1, since the template ontology OH includes child classes “b1”, “b2”, and “b3”, the number of members of the child class set of the template ontology OH in the child class set G1 is 3 In the child class set G1, the personal ontology OA includes child classes “b1” and “b2”, and the personal ontology OB includes child classes “b1”, “b2”, and “b3”. Therefore, the child classes commonly included in the personal ontologies OA and OB are only “b1” and b2 ”, and the number of members of the product set of the child class sets of the personal ontology OA and OB in the child class set G1 is 2. As a result, the degree of approximation between the child ontology sets G1 of the personal ontology OA and OB is 2/3.

Similarly, the degree of approximation between the personal ontology OA and OB between the child class sets G2 is 1/2, the degree of approximation between the child class sets G3 is 0/2, and the degree of approximation between the child class sets G4 is 2/2. As a result, personal ontology OA, similarity S _T topology between OB becomes 2/3 + 1/2 + 0/2 + 2/2.
In the calculation of the approximate degree of topology between the personal ontology OA and OB, the class ID assigned from the template ontology OH is referred to, and the one along the connection form of the template ontology OH exists between the personal ontology OA and OB. You only have to confirm whether you want to do it. For example, whether the connection form a1-b1-c1 in the personal ontology OA, OB is the same as the connection form of the template ontology OH depends on whether the classes “a1”, “b1”, and “c1” are the personal ontology OA, OB. It can be determined simply by confirming whether or not it is held at.

  For this reason, in order to confirm the degree of coincidence of the topology between the personal ontology OA and OB, it is only necessary to check the class ID possessed by the personal ontology OA and OB. Therefore, it is possible to reduce the calculation amount of the degree of approximation of the topology between the personal ontology OA and OB.

Next, the degree of approximation between the common classes of the personal ontology OA and OB is calculated. Here, when calculating the degree of approximation between the common classes of the personal ontology OA and OB, an instance set belonging to the class can be used. That is, in a certain class C1, if the instance set of the source ontology is x, the instance set of the target ontology is y, and the instance set of the template ontology OH is z, the degree of approximation between the common classes of the personal ontology OA and OB is | x ∩y | / | z |. By adding the degree of approximation between the common class, it is possible to measure the degree of approximation S _C between personal ontology OA, the OB classes.

  For example, in the common class “b2” between the personal ontologies OA and OB, the instance set G6 in the common class “b2” of the personal ontology OA includes the instance “n”, and the common class “b2” of the personal ontology OB. An instance “n” exists in the instance set G6, and an instance “n” exists in the common class “b2” of the template ontology OH. As a result, the number of members of the common class “b2” of the template ontology OH is 1, the number of members of the product set of the instance set G6 of the personal ontology OA and OB is 1, and the degree of approximation between the common classes “b2” is 1/1.

  In the common class “c2” between the personal ontology OA and OB, the instance set G7 in the common class “c2” of the personal ontology OA includes instances “a”, “c”, “b”, and “d”. In the instance set G7 in the common class “c3” of the personal ontology OB, instances “a”, “c”, “e”, and “f” exist, and in the common class “c3” of the template ontology OH “A”, “e”, “c”, “f”, “b”, “d”, and “g” exist.

As a result, the number of members of the common class “c3” of the template ontology OH is 7, the number of members of the product set of the instance set G7 of the personal ontologies OA and OB is 2, and the degree of approximation between the common classes “c3” is 2/7.
Accordingly, the degree of approximation S _C between the classes of the personal ontology OA and OB is 1/1 + 2/7.

Note that even in the calculation of the degree of approximation between the common classes of the personal ontology OA and OB, it is only necessary to confirm whether the instance ID allocated from the template ontology OH exists between the personal ontology OA and OB. For this reason, in order to calculate the degree of approximation between the classes of the personal ontology OA and OB, it is not necessary to confirm in advance the correspondence between instances based on the consistency of the names of instances, and the amount of calculation can be reduced.
Then, the personal ontology OA, the similarity S _C between the degree of approximation S _T and class topology in OB is determined, by using an evaluation function f (X) corresponding to the importance for the topology and class, the following expression Thus, the degree of approximation S _O (AB) between the personal ontology OA and OB can be given.
S _O (AB) = S _T + f (S _C )

  This makes it possible to construct personal ontology OA and OB while inheriting the class characteristics possessed by the template ontology OH, and directly measure the co-occurrence between instance sets to the degree of approximation between the personal ontology OA and OB. It is possible to appropriately extract personal ontology OA and OB adapted to individual tastes while suppressing the amount of calculation, and the domain ontology OH possesses knowledge of the domain possessed by the template ontology OH. It can be used as a relative measure of possession, and users who have a lot of knowledge about the domain can be effectively narrowed down.

The personal ontology OA, when measuring the degree of approximation S _T topology in OB, based on the number of members of the class set stationery ontology, personal ontology OA, by measuring the degree of approximation topology between OB, personal ontology Even when the number of members of the class set of OA and OB increases, the degree of topology approximation between the personal ontology OA and OB can be prevented from decreasing, and the amount of knowledge is large in the sense that there are abundant classes. The degree of approximation with the personal ontology OA, OB can be increased.

FIG. 3 is a diagram illustrating the superiority of the method for measuring the degree of approximation between personal ontology according to an embodiment of the present invention.
In FIG. 3, it is assumed that the ontology OC does not have the class “b3” of the ontology OB in FIG. In this case, in ontology OB, child classes “b1”, “b2”, and “b3” are included in child class set G1, and in ontology OC, child classes “b1” and “b2” are included in child class set G1. Despite being included, the degree of approximation between the child class sets G1 of the personal ontology OA and OC is 2/3, similar to the degree of approximation between the child class sets G1 of the personal ontology OA and OB.

  As a result, in the ontology OC, the degree of approximation between the personal ontology OA and OC is greater than the degree of approximation between the personal ontology OA and OB, even though there is no class “b3” of the ontology OB in FIG. The degree of approximation with a personal ontology having a large amount of knowledge can be prevented from becoming relatively small in the sense that there are abundant classes.

FIG. 4 is a diagram illustrating a community formation method using the approximation of interest ontology according to an embodiment of the present invention.
In FIG. 4, it is assumed that the blog entries PA and PB of the users A and B are classified with respect to the template ontology, thereby generating the interest ontologies KA and KB of the users A and B, respectively (step S1). . Then, the degree of approximation between the interest ontologies KA and KB of the users A and B is measured (step S2), and the topologies are analyzed by analyzing classes and instances that co-occur between the interest ontologies KA and KB having a high degree of approximation. Although it is different, information that is highly likely to be interesting can be recommended to the user as unexpected information through the other user's entry (step S3).

For example, by measuring the degree of approximation between the interest ontologies KA and KB of each user A and B, a user who is interested in a class such as “Madchester” or an instance such as “Happy Mondays” can have a class “Glasgow” It can be seen that there is a high possibility of being interested in an instance of “Teenage Fanclub”.
Moreover, by applying such interest ontologies KA and KB to a blog, it is possible to support the formation of a community based on an unexpected entry recommendation based on the degree of approximation between the interest ontologies KA and KB rather than a simple keyword search. The user's interest can be naturally expanded (step S4).

  The present invention makes it possible to easily create a personal ontology and quickly obtain information that matches one's interest, and automatically and efficiently information that matches one's interest from an information source of an information communication system. Can be used.

1 is a block diagram showing a schematic configuration of a system to which an ontology approximation measuring device according to an embodiment of the present invention is applied. It is a figure which shows the approximation measuring method between personal ontology concerning one Embodiment of this invention. It is a figure which shows the predominance of the approximation measuring method between personal ontology concerning one Embodiment of this invention. It is a figure which shows the community formation method using the approximation of the interest ontology which concerns on one Embodiment of this invention. It is a figure which shows the structural example of the ontology constructed | assembled in the conventional completely distributed environment. It is a figure which shows an example of the approximation measuring method by the brute force method between the classes of FIG. It is a figure which shows an example of the approximation measuring method by the brute force method between the classes of FIG. It is a figure which shows an example of the mapping result between the classes of FIG. It is a figure which shows an example of the approximation degree measurement result between the ontology of FIG. It is a figure which shows the comparative example of the approximation degree measurement result with respect to the conventional some ontology.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Communication network 2-4 Terminal 5 Server 5a Frequent word extraction means 5b Classifier application means 5d Approximation measurement means 5c Personal ontology extraction means 6 Model ontology 7-9 Blog site 7a-7n, 8a-8n, 9a-9n Blog entry

Claims (4)

Word extraction means for extracting words included in the interest information stored in the storage area and constructed for each individual;
Classifier applying means for extracting a class or an instance including a word extracted by the word extracting means from a template ontology that is stored in a storage area and has previously set words that are conceptually hierarchized;
A personal ontology extracting means for extracting, from each model ontology, a hierarchical structure including the extracted class or instance and higher class thereof as a personal ontology and storing it in a storage area for each individual;
An approximation measuring means for measuring the approximation between the topologies of two different personal ontology stored in the storage area with respect to a topology representing a connection form between classes forming the hierarchical structure in the personal ontology; ,
The approximation measuring means includes:
A set of child classes having a single class as a parent class is defined as a child class set, and a child class set having a common class including the same word in two different personal ontology as the parent class is defined as X and Y, and the template ontology , Where Z is a child class set whose parent class is a class including the same word as the common class, the class number belonging to the product set of the child class sets X and Y is the class belonging to the child class set Z The value divided by the number of members is used as the degree of approximation between the child class sets X and Y, and all child class sets having a common class including the same word as a parent class in two different personal ontologies between the child class sets. Means for calculating the degree of approximation;
Means for calculating an approximation of the ontology , which calculates a sum of the approximations between all the child class sets calculated as an approximation between the topologies of the personal ontology . Word extraction means for extracting words included in the interest information stored in the storage area and constructed for each individual;
Classifier applying means for extracting a class or an instance including a word extracted by the word extracting means from a template ontology that is stored in a storage area and has previously set words that are conceptually hierarchized;
A personal ontology extracting means for extracting, from each model ontology, a hierarchical structure including the extracted class or instance and higher class thereof as a personal ontology and storing it in a storage area for each individual;
An approximation degree measuring means for measuring an approximation degree between common classes including the same word in two different personal ontology stored in the storage area,
The approximation measuring means includes:
A set of instances belonging to one class is defined as an instance set.
The instance set belonging to the common class including the same word in two different personal ontology is x and y, and the instance set belonging to the class including the same word as the common class in the template ontology is z. A value obtained by dividing the number of members of an instance belonging to the set of sets x and y by the number of members of an instance belonging to the instance set z is used as an approximation between the instance sets x and y, and the same word in two different personal ontologies Means for calculating the degree of approximation between the instance sets for all instance sets belonging to a common class including:
Means for calculating an approximation of the ontology between all of the calculated instance sets as an approximation between the classes of the personal ontology . A method for measuring the degree of approximation of an ontology executed by an approximation degree measuring device that measures the degree of approximation between two different personal ontology,
Extracting words contained in the blog entry by applying morphological analysis to the blog entry stored in the storage area ;
A step of words that are stored in the storage area and set in advance to select a template ontology formed by conceptual layered,
From the template ontology, the steps to extract the class or instance including words extracted from the blog entry,
For each individual, extracting the extracted class or instance and a hierarchical structure including the higher class thereof from the template ontology as a personal ontology and storing it in a storage area ;
For the topology representing the connection form between classes forming the hierarchical structure in the personal ontology, the step of measuring the degree of approximation S _T between the topology of the two personal ontology different stored in the storage area,
A step of measuring the degree of approximation S _C between common class that contains the same words in the two personal ontology different stored in the storage area,
When the degree of approximation between two different personal ontology is S ₀ (AB) and the evaluation function according to the importance for the topology and class is f (X), the degree of approximation S ₀ (AB) is represented by S ₀ ( AB AB) = S _T + f (S _C ) , and
The step of measuring the degree of approximation S _T between the topology,
A set of child classes having a single class as a parent class is defined as a child class set, and a child class set having a common class including the same word in two different personal ontology as the parent class is defined as X and Y, and the template ontology , Where Z is a child class set whose parent class is a class including the same word as the common class, the class number belonging to the product set of the child class sets X and Y is the class belonging to the child class set Z The value divided by the number of members is used as the degree of approximation between the child class sets X and Y, and all child class sets having a common class including the same word as a parent class in two different personal ontologies between the child class sets. Calculating a degree of approximation;
Comprising a step of computing the sum of the degree of approximation between all child class set of calculation as the approximation degree S _T between the topology of the personal ontology, and
The step of measuring the degree of approximation S _C between the classes,
A set of instances belonging to one class is defined as an instance set.
The instance set belonging to the common class including the same word in two different personal ontology is x and y, and the instance set belonging to the class including the same word as the common class in the template ontology is z. A value obtained by dividing the number of members of an instance belonging to the set of sets x and y by the number of members of an instance belonging to the instance set z is used as an approximation between the instance sets x and y, and the same word in two different personal ontologies Calculating the degree of approximation between the instance sets for all instance sets belonging to a common class including:
Similarity measuring method ontology and a step of calculating a sum of the similarity between all of the instances set was calculated as the degree of approximation S _C between classes of personal ontology, the. Extracting words contained in the blog entry by applying morphological analysis to the blog entry stored in the storage area ;
A step of words that are stored in the storage area and set in advance to select a template ontology formed by conceptual layered,
From the template ontology, the steps to extract the class or instance including words extracted from the blog entry,
For each individual, extracting the extracted class or instance and a hierarchical structure including the higher class thereof from the template ontology as a personal ontology and storing it in a storage area ;
For the topology representing the connection form between classes forming the hierarchical structure in the personal ontology, the step of measuring the degree of approximation S _T between the topology of the two personal ontology different stored in the storage area,
A step of measuring the degree of approximation S _C between common class that contains the same words in the two personal ontology different stored in the storage area,
When the degree of approximation between two different personal ontology is S ₀ (AB) and the evaluation function according to the importance for the topology and class is f (X), the degree of approximation S ₀ (AB) is represented by S ₀ ( AB AB) = S _T + f (S _C ), and an ontology approximation degree measurement program for causing a computer to execute the step,
The step of measuring the degree of approximation S _T between the topology,
A set of child classes having a single class as a parent class is defined as a child class set, a set of child classes having a common class including the same word in two different personal ontologies as the parent class is X and Y, and the template ontology , Where Z is a child class set whose parent class is a class including the same word as the common class, the class number belonging to the product set of the child class sets X and Y is the class belonging to the child class set Z The value divided by the number of members is used as the degree of approximation between the child class sets X and Y, and all child class sets having a common class including the same word as a parent class in two different personal ontologies between the child class sets. Calculating a degree of approximation;
Comprising a step of computing the sum of the degree of approximation between all child class set of calculation as the approximation degree S _T between the topology of the personal ontology, and
The step of measuring the degree of approximation S _C between the classes,
A set of instances belonging to one class is defined as an instance set.
The instance set belonging to the common class including the same word in two different personal ontology is x and y, and the instance set belonging to the class including the same word as the common class in the template ontology is z. A value obtained by dividing the number of members of an instance belonging to the set of sets x and y by the number of members of an instance belonging to the instance set z is used as an approximation between the instance sets x and y, and the same word in two different personal ontologies Calculating the degree of approximation between the instance sets for all instance sets belonging to a common class including:
Similarity measuring program ontology, characterized in that it comprises a step of computing the sum of the similarity between all of the instances set was calculated as the degree of approximation S _C between classes of personal ontology, the.

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