JP2008027143A - Ontology approximation level measuring device, ontology approximation level measuring method and ontology approximation level measuring program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To measure inter-ontology approximation level by sufficiently reflecting the interest of a user while suppressing calculation quantity. <P>SOLUTION: An interest level measuring means 5d measures an interest level in classes or instances extracted by a personal ontology extraction means 5c based on the rate of the content of those classes or instances in blog entries 7a to 7n, 8a to 8n and 9a to 9n, and an approximation level measuring means 5e measures inter-personal ontology approximation level under the consideration of the topology of a personal ontology extracted by the personal ontology extraction means 5c with the interest level measured by the interest level measuring means 5d. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an ontology approximation measuring device, an ontology approximation measuring method, and an ontology approximation measuring program, and in particular, automatically refers to a personal ontology in which personal interest information is conceptually layered while referring to a blog entry. After being generated, it is suitable for application to a method for efficiently measuring the degree of approximation between personal ontology.

When performing an information search on the Internet or the like, an information search is mainly performed by inputting a keyword. In this keyword search, for example, in the field of video recording devices, since the recording content is specified by a keyword, the intended video content could not be recorded unless the user came up with an appropriate keyword.
Further, even in a search using a search engine such as google, if the user does not come up with an appropriate keyword, the intended content cannot be searched, and many unnecessary search results may be included.

Furthermore, even in blog providers such as doblog, the user remains at the level of searching for information of interest on a keyword basis and accessing the blog site according to the search result, and the unknown keyword or community in which the user is interested. Or I couldn't find a blog site.
One reason for this is that the current search method is only keyword search, and a high-precision search using the class (concept) system and class attributes, that is, class names and instances (substances) cannot be performed. It is done. The keyword is just a character string, but the ontology class has multiple instances as members, and the individual preference is reflected in the class depending on what instance is a member of the class. Can do. Also, the preference of each individual can be reflected in the class depending on the class hierarchy.
Here, 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. 7 is a diagram illustrating a configuration example of an ontology constructed in a conventional completely distributed environment.
In FIG. 7, 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 “UK”, and the child class “America” has a child class “Indies” in the child class “America”. It is assumed that instances “nirvana” and “Stone temple pilots” exist in the child class “indies”.

Further, a child class “UK” has a child class “Alternative”, and a child class “Alternative” has a child class “Scotland”. Assume 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. 8 and FIG. 9, mapping between ontology OA and OB can be performed by calculating the approximation 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 possessed by a class.

FIG. 10 is a diagram illustrating an example of a mapping result between the classes of FIG.
In FIG. 10, as a result of mapping between ontology OA and OB, ontology OA “lock” class and ontology OB “lock” class are mapped, ontology OA “US” class and ontology OB “USA” ”Is mapped, ontology OA“ indie ”class and ontology OB“ indies ”class are mapped, ontology OA“ grunge ”class and ontology OB“ indies ”class are 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. 11 is a diagram illustrating an example of the approximation degree measurement result between the ontology in FIG.
In FIG. 11, it is assumed that the mapping result of FIG. 10 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. 11, 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. 12 is a diagram illustrating a comparative example of the degree-of-approximation measurement results for a plurality of conventional ontology.
In FIG. 12, 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.
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.

Furthermore, in the conventional method for measuring the degree of approximation between ontologies, users who are eagerly talking about an artist, one user is eagerly talking about the artist, and another user is eagerly talking about the artist. Although it may not be the case, since the degree of interest in the artist is measured to be the same, the degree of approximation between ontology cannot be measured while sufficiently reflecting the user's interest. There was a problem.
Therefore, an object of the present invention is to provide an ontology approximation measuring device, an ontology approximation measuring method, and an ontology approximation measuring method capable of calculating an approximation between ontology while sufficiently reflecting a user's interest while suppressing a calculation amount. To provide an ontology approximation measurement program.

  In order to solve the above-described problem, according to the ontology approximation degree measuring device according to claim 1, the class or instance constituting the ontology is based on a ratio included in an entry reflecting an individual's interest. An interest degree measuring means for measuring an interest degree for a class or an instance, and an approximation degree measuring means for measuring an approximation degree between the ontology based on the interest degree measured by the interest degree measuring means. And

  Further, according to the ontology approximation degree measuring apparatus according to claim 2, the word extraction means for extracting words included in the interest information constructed for each individual, and the template ontology in which the interest information is conceptually hierarchized The classifier application means for extracting a class or instance including a word, the personal ontology extraction means for extracting the extracted class or instance and their higher class from the template ontology as a personal ontology, and the interest of the individual is reflected. An interest degree measuring means for measuring the degree of interest in the class or instance based on the ratio of the class or instance included in the entry, and the personal ontology based on the interest degree measured by the interest degree measuring means. And an approximation degree measuring means for measuring the degree of approximation between The features.

  In addition, according to the ontology approximation measurement method according to claim 3, the step of extracting words included in the interest information constructed for each individual, and the word from the template ontology in which the interest information is conceptually hierarchized. Extracting a class or instance including the step of extracting the extracted class or instance and a class higher than the extracted class or instance as a personal ontology from the template ontology; The step of measuring the degree of interest with respect to the class or instance based on the included ratio, and the step of measuring the degree of approximation between the personal ontology based on the measured degree of interest.

  According to the ontology approximation measurement program according to claim 4, the step of extracting words included in the interest information constructed for each individual, and the word from the template ontology in which the interest information is conceptually hierarchized. Extracting a class or instance including the step of extracting the extracted class or instance and a class higher than the extracted class or instance as a personal ontology from the template ontology; Making the computer execute a step of measuring the degree of interest in the class or instance based on the included ratio, and a step of measuring the degree of approximation between the personal ontology based on the measured degree of interest And

  As described above, according to the present invention, it is possible to accurately search for information suitable for one's preference based on consistency between concepts, not just a character string, taking into account the strength of personal interest. It becomes possible, and information suitable for one's preference can be widely distributed on the Internet, and a community suitable for individual preference 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 communication network 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. Personal ontology extraction means 5c for extracting the extracted class or instance and all higher classes thereof from the template ontology 6 as personal ontology 6, and the class or instance extracted by the classifier applying means 5b are blog entries 7a to 7n, Based on the percentages included in 8a-8n, 9a-9n, An interest degree measuring means 5d for measuring the degree of interest in these classes or instances, and an approximation degree measuring means 5e for measuring the degree of approximation between personal ontology based on the interest degree measured by the interest degree measuring means 5d are provided. It has been.

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

  Also, the interest level measuring means 5d determines the class or instance extracted by the personal ontology extracting means 5c based on the ratio of the blog entries 7a to 7n, 8a to 8n, and 9a to 9n. Measure interest. Then, the degree-of-approximation measuring means 5e calculates the degree of approximation between personal ontology while taking into consideration the topology of the personal ontology extracted by the personal ontology extracting means 5c together with the degree of interest measured by the interest degree measuring means 5d. It can be measured.

This makes it possible to accurately search for information that suits one's preference based on the consistency between concepts, not just a character string, taking into account the strength of personal interests. Suitable information can be widely distributed on the Internet, and a community adapted to personal tastes can be formed.
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.

The frequent word extracting means 5a, classifier applying means 5b, personal ontology extracting means 5c, interest degree measuring means 5d, and approximation degree measuring means 5e are programs having instructions for executing the processing performed by these means. This can be realized by causing a computer to execute it.
If this program is stored in a storage medium such as a CD-ROM, the frequent word extraction means 5a and classifier application means are installed by installing the storage medium in the computer of the server 5 and installing the program in the computer. 5b, the processing performed by the personal ontology extracting means 5c, the interest degree measuring means 5d, and the approximation degree measuring means 5e can be realized. Moreover, by downloading this program via the communication network 1, this program can be easily spread.

  In addition, when a computer executes a program in which an instruction for performing processing performed by the frequent word extraction unit 5a, the classifier application unit 5b, the personal ontology extraction unit 5c, the interest degree measurement unit 5d, and the approximation degree measurement unit 5e is described. It may be executed by a stand-alone computer, or may be distributed to a plurality of computers connected to a network.

FIG. 2 is a diagram illustrating a method for generating a personal ontology according to an embodiment of the present invention.
In FIG. 2, an entry set of users A, B,..., X is collected through a ping server or the like, and an index is created by performing morphological analysis on all the collected blog entries ( Step S1).
Next, all the blog entries collected by the ping server are classified with respect to the template ontology OH (step S2). Here, as a method for classifying blog entries, if there is a description in an entry having a name attribute of class C _{i having} a template ontology OH, the entry can be classified into class C _i . Also, if the name attribute of the instance I _i (∈C _i ) belonging to the class C _i having the template ontology OH is in the description in the entry, the entry is classified into the instance I _i belonging to the class C _i. Can do. Note that the same entry may be classified into a plurality of classes.

For example, if the character string “Charlantans” is in the description in the entry, the entry can be classified into an instance “Charlantans” of the class “Madchester”.
Next, the number of users A, B,..., X who are interested in each instance of the lowermost class C ₁ forming the template ontology OH is measured (step S3). When the number of users A, B,..., X who are interested in instances of class C _l is measured, the number of users even when the same user describes instance I _l in a plurality of entries. Measures 1

Next, the user A interested in lowermost class C _l to form a stationery ontology OH, measured B, · · ·, the number of X. Here, the number of users A, B,..., X interested in the lowest class C ₁ forming the template ontology OH is the number of users interested in all instances under the lowest class C _1. , it can be calculated by the sum of the number of users interested in the lowest layer class C _l itself. Note that the number of users is measured as 1 even when the same user is interested in a plurality of instances or is interested in the lowermost class and instances belonging to the lowermost class at the same time. In this way, by recursively measuring the number of users A, B,..., X interested in the class or instance forming the template ontology OH up to the root class, the user A interested in the domain , B,..., X can be calculated.

Next, when all the blog entries collected by the ping server are classified with respect to the template ontology OH, the classification results are arranged for each user ID, whereby each user A, B,. Each interest ontology OA,..., OX is generated (step S4).
Here, when all the blog entries collected by the ping server are classified with respect to the template ontology OH, the instances that belong to the same class of the ontology have the same property, and between classes close to the class hierarchy The classification error can be eliminated by using the property that the properties of are close and the properties of both instances are also close.

FIG. 3 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. 3, classes “b1”, “b2”, and “b3” exist immediately below the class “a1” of the template ontology OH, and “c1” and “c1” 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.
Therefore, the degree of approximation _{S C} between personal ontology OA, the OB class 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 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.
In the example of FIG. 3, the method for measuring the degree of approximation between the personal ontology OA and OB without considering the user's interest level has been described. The degree of approximation between the personal ontology OA and OB can also be measured in consideration of the above.

Here, the degree of interest in a class or instance can be defined as follows, for example.
(1) The degree of interest of the user per entry is 1.
(2) If the types of user classes and instances appearing in an entry E _i are N (E _i ), the degree of interest of each class and instance of the user in the entry E _i is 1 / N (E _i ). is there.
(3) The degree of interest for each instance I _i in the ontology is given by the following equation, where E is the total accumulated entry set of the user.

(4) The degree of interest for each class C _i in the ontology is given by the following equation, where E is the total accumulated entry set of the user.

(5) The interest level of the instance is inherited by the interest level of the class to which the instance belongs, and the interest level of the child class is inherited by the interest level of the parent class.
FIG. 4 is a diagram illustrating an example of the degree of interest of classes and instances according to an embodiment of the present invention.
In FIG. 4, it is assumed that entries E ₁ and E ₂ are included in all the stored entries of user A. In the interest ontology OA of the user A, a class called “alternative” exists, and a class called “alternative” has child classes called “Madchester” and “Shoegaze”. The class “alternative” has an instance “nirvana”, the class “Madchester” has instances “New Order” and “Stone Roses”, and the class “Shoegaze” has “ Assume that instances of “My Bloody Valentine” and “Ride” exist.

In entry E ₁ , four artists “Stone Roses”, “My Bloody Valentine”, “nirvana”, and “New Order” appear, and in entry E ₂ , one artist “My Bloody Valentine” appears again. Above, one genre “Shoegaze” and one artist “Ride” appear.

  Therefore, the interest level of the instance “New Order” is 1/4, the interest level of the instance “Stone Roses” is 1/4, and the interest level of the instance “My Bloody Valentine” is 1/4 + 1/3 = 7 / 12. The interest level of the instance “Ride” is 1/3, and the interest level of the instance “nirvana” is 1/4.

  The interest level of the class “Madchester” can be obtained by adding the interest levels of the instances “New Order” and “Stone Roses” under the class, and 1/4 + 1/4 = 1/2. It becomes. Further, the interest level of the class “Shoegaze” can be obtained by adding the interest levels of the instances “My Bloody Valentine” and “Ride” under the class, and 7/12 + 1/3 = 5/4 It becomes. Furthermore, the interest level of the class “alternative” can be obtained by adding the interest levels of the instances “nirvana”, “Madchester”, and “Shoegaze” under the class, 1/2 + 5/4 + 1 / 4 = 2.

As a result, it is possible to recognize that the user A's interest is strong because the artist “My Bloody Valentine” has been written in the two entries E ₁ and E ₂ . In addition, regarding the genre of “Shoegaze”, the class name also appears in entry E ₂ and the subordinate artists are frequently spoken in entries E ₁ and E ₂ , so the degree of interest can be increased. Even if there are many artists under their control, it can be prevented that they are treated equally as interests.

FIG. 5 is a diagram illustrating a method for measuring the degree of approximation between personal ontology considering the degree of interest according to an embodiment of the present invention.
In FIG. 5, there are “b1” and “b2” classes immediately below the “a1” class of the user A's interest ontology OA, and “c3” and “c4” immediately below the “b2” class. The class “b1” has instances “m” and “k”, and the class “b2” has an instance “n”. The class “c3” has instances “a”, “b”, and “c”, and the class “c4” has instances “g” and “h”. To do.

In the ontology OA, the interest degree of the class “c3” is 1, the interest degree of the instance “a” is 2, the interest degree of the instance “b” is 5, and the interest degree of the instance “c” is 4. If there is, the degree of interest under the class “c3” is 1 + 2 + 5 + 4 = 12.
Also, in the ontology OA, if the interest level of the class “c4” is 0, the interest level of the instance “g” is 2, and the interest level of the instance “h” is 1, the subordinate of the class “c4” The degree of interest is 0 + 2 + 1 = 3.

In the ontology OA, if the interest level of the class “b2” is 1, and the interest degree of the instance “n” is 2, the interest degree under the class “b2” is 12 + 3 + 1 + 2 = 18.
Also, in the ontology OA, if the interest level of the class “b1” is 0, the interest degree of the instance “m” is 3, and the interest degree of the instance “k” is 2, the subordinate of the class “b1” The degree of interest is 0 + 3 + 2 = 5.

In the ontology OA, if the interest level of the class “a1” is 0, the interest level under the class “a1” is 5 + 18 = 23.
In addition, classes “b1”, “b2”, and “b3” exist immediately below the class “a1” of the user B's interest ontology OB, and a class “c1” exists immediately below the class “b1”. And a class “c3” and a class “c4” exist immediately under the class “b2”, and an instance “l” exists in the class “c1”. The class “b2” has an instance “n”, the class “b3” has an instance “d”, the class “c3” has “a”, “c”, and It is assumed that an instance “e” exists, and a class “c4” includes instances “p” and “i”.

In the ontology OB, the interest degree of the class “c3” is 0, the interest degree of the instance “a” is 3, the interest degree of the instance “c” is 3, and the interest degree of the instance “e” is 3. If there is, the degree of interest under the class “c3” is 3 + 3 + 3 = 9.
Also, in the ontology OB, if the interest level of the class “c4” is 1, the interest degree of the instance “p” is 3, and the interest degree of the instance “i” is 2, the subordinate of the class “c4” Is 1 + 3 + 2 = 6.

In the ontology OB, if the interest level of the class “b3” is 0 and the interest degree of the instance “d” is 2, the interest degree under the class “b3” is 0 + 2 = 2.
Further, in the ontology OB, if the interest level of the class “b2” is 0 and the interest degree of the instance “n” is 6, the subordinate interest level of the class “b2” is 6 + 9 + 6 = 21.
In the ontology OB, if the degree of interest of the class “b1” is 0, the degree of interest under the class “b1” is 0 + 3 = 3.
In the ontology OB, if the degree of interest of the class “a1” is 0, the degree of interest under the class “a1” is 3 + 21 + 2 = 26.

Here, the terms used for calculating the degree of interest matching between ontology are defined as follows. Let T ₁ be the topology consisting of the parent class and child class, and T ₂ be the topology consisting of the class and instance. Further, a common class of interest ontology OA and OB is defined as C _i, and a common instance is defined as I _i ∈I. In particular, a class set belonging to topology T ₁ is C (T ₁ ), and a class set belonging to topology T ₂ is C (T ₂ ). Further, the interest coincidence for the common class C _i is I (C _i ), the interest coincidence for the common instance I _i is I (I _i ), and the interest coincidence under the common class C _i is I _t (C _i). ).

(1) Then, the common class C _i of the interest ontology OA, OB is analyzed, and the common class C _i forming both topologies T ₁ and T ₂ exists, and the common class forming the topology T ₁ is noted. C _i and common class C _i forming topology T ₂ are extracted. For example, in the example of FIG. 5, the common class a1, b1, b2 forms a topology _{T 1,} the common class b2, c3, c4 can form a topology _{T 2.}

(2) common classes _{C i} interested ontology OA, when to have a common instance _{I i} between OB, the interest degree of coincidence with respect to a common instance _{I i} I _{(I i)} is interest degree of interest ontology OA, OB instance of Can be the smaller of the two. For example, in the example of FIG. 5, the interest coincidence with respect to the common instance a under the common class c3 is 2, and the interest coincidence with respect to the common instance c under the common class c3 is 3.

(3) The interest coincidence I (C _i ) for the common class C _i in the interest ontologies OA and OB can be the smaller of the interest degrees for the classes of the interest ontologies OA and OB. For example, in the example of FIG. 5, the interest coincidence for the common class c3 is 0, and the interest coincidence for the common class c4 is 1.
(4) The interest coincidence I _t (C _i ) under the common class C _i εC (T ₁ ) is the product set of child classes under the class C _i in the interest ontology OA and OB as N (C _i ). When the union is U (C _i ), it can be given by the following equation.

For example, in the example of FIG. 5, the interest coincidence I _t (C _i ) under the common class b2 is (9 + 3) / 2 = 6.
Then, as shown by the following equation, all common classes C _i value obtained by adding an interest matching score I _{t (C i)} for forming a topology T _1, to a common class set forming a topology T ₁ The degree of interest coincidence S (T ₁ ) can be used.

(5) On the other hand, the interest coincidence I _t (C _i ) under the common class C _i ∈ C (T ₂ ) is defined as an instance set under the class C _i in the interest ontology OA as I _A (C _i ), If the instance set under the class C _i in the ontology OB is I _B (C _i ), it can be given by the following equation.

For example, in the example of FIG. 5, the interest coincidence degree I _t (C _i ) under the common class c3 is (2 + 0 + 3 + 0) / 4 = 5/4.
Then, as shown by the following equation, all common classes C _i value obtained by adding an interest matching score I _{t (C i)} for forming a topology T _2, to a common class set forming a topology T ₂ The degree of interest coincidence S (T ₂ ) can be used.

(6) If the evaluation function corresponding to the importance for the topologies T ₁ and T ₂ is f (X), the degree of approximation between the interest ontology OA and OB considering the degree of interest is S _O (AB) = S ( T ₁ ) + f (S (T ₂ )).
Then, the degree-of-approximation measuring means 5e in FIG. 1 measures the degree of approximation between personal ontology while considering not only the topology of the personal ontology extracted by the personal ontology extracting means 5c but also the degree of interest of the user. Thus, it is possible to accurately search for information suitable for one's preference based on consistency between concepts, not just a character string, taking into account the strength of personal interest.

FIG. 6 is a diagram illustrating a community formation method using the approximation of interest ontology according to an embodiment of the present invention.
In FIG. 6, 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 S11). . Then, the degree of approximation between the interest ontologies KA and KB of the users A and B is measured (step S12), 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 of interest can be recommended to the user as unexpected information through the entry of another user (step S13).

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 S14).

  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 production | generation method of the personal ontology based on one Embodiment of this invention. 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 an example of the interest degree of the class and instance which concern on one Embodiment of this invention. It is a figure which shows the approximation measuring method between personal ontology which considered the degree of interest based on 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 5c Personal ontology extraction means 5d Interest measurement means 5e Approximation degree measurement means 6 Model ontology 7-9 Blog site 7a-7n, 8a-8n, 9a-9n Blog entry

Claims (4)

An interest degree measuring means for measuring an interest degree for the class or instance based on a ratio of the class or instance constituting the ontology included in the entry reflecting the interest of the individual;
An ontology approximation degree measuring apparatus comprising: an approximation degree measuring means for measuring an approximation degree between the ontology based on the interest degree measured by the interest degree measuring means. A word extraction means for extracting words included in the interest information constructed for each individual;
Classifier application means for extracting a class or instance containing the word from a template ontology in which interest information is conceptually hierarchized;
Personal ontology extraction means for extracting the extracted class or instance and the higher class thereof from the template ontology as a personal ontology;
An interest degree measuring means for measuring an interest degree of the class or instance based on a ratio of the class or instance being included in an entry reflecting an individual's interest;
An ontology approximation degree measuring apparatus comprising: an approximation degree measuring means for measuring an approximation degree between the personal ontology based on the interest degree measured by the interest degree measuring means. Extracting words included in the interest information constructed for each individual;
Extracting a class or instance containing the word from a template ontology in which interest information is conceptually hierarchized;
Extracting the extracted classes or instances and their superior classes from the template ontology as personal ontology;
Measuring the degree of interest in the class or instance based on the percentage of the class or instance in an entry reflecting an individual's interest;
And a step of measuring the degree of approximation between the personal ontology based on the measured degree of interest. Extracting words included in the interest information constructed for each individual;
Extracting a class or instance including the word from a template ontology in which interest information is conceptually hierarchized;
Extracting the extracted classes or instances and their superior classes from the template ontology as a personal ontology;
Measuring the degree of interest in the class or instance based on the percentage of the class or instance included in an entry reflecting an individual's interest;
An ontology approximation degree measurement program that causes a computer to execute the step of measuring the degree of approximation between the personal ontology based on the measured degree of interest.

Images