KR101016110B1 - System and method for extracting ontology instance using ontology property - Google Patents

Abstract

The present invention relates to an ontology instance extraction system and method using ontology properties, the machine (for example, a computer program) can perform a process based on semantics using the properties of the ontology from structural data and unstructured data, etc. An ontology instance extraction system using ontology properties for extracting a formal ontology instance and a method thereof are provided.

To this end, the present invention is an ontology instance extraction system, comprising: ontology storage means for storing the already built ontology; Structural data analysis means for generating ontology-based triple data from the structural data using the ontology property of the ontology storage means; Unstructured data analysis means for generating ontology based triple data from unstructured data using the ontology property of the ontology storage means; And ontology instance extracting means for extracting ontology instances from the ontology-based triple data generated by the structural data analyzing means and the unstructured data analyzing means by using the ontology property of the ontology storing means.

Ontology, instance, property, semantic web, integration, structural, unstructured

Description

System and method for extracting ontology instance using ontology property}

The present invention relates to an ontology instance extraction system and method using ontology properties for extracting ontology instances using properties of ontology from structural data and unstructured data, more specifically, exponentially increased including web documents. Ontology instance extraction using ontology properties to automatically extract ontology instances that allow the computer to understand and process the contents of the document using structural properties and unstructured data in an electronic document environment. A system and method thereof are provided.

The development of the Internet, especially the web, has exploded in structured and unstructured data. However, existing information systems have no choice but to process information by using external information such as what characters appear in the document or how long the document is, regardless of the meaning or content of the document. Because of the limited number of documents in the existing intranet environment, the information system helped to search and process the documents, and it was up to the user to find and process the correct documents by checking the contents of the actual documents. However, the exponentially increasing amount of documents in the Web environment makes it difficult for people to see the contents of documents.

As a solution, the "Semantic Web" has been proposed to give well-defined common meanings to information on the Web so that not only people but also computer programs can understand and process the contents of documents. And "Ontology", a well-defined common semantic system, was built one by one for this semantic web. Semantic web documents created using the semantics defined in the ontology can be processed by computer programs by understanding the contents of the document, thereby enabling information systems to search or process the document based on the content.

The problem, however, is the transformation of already structured and unstructured and unstructured data into semantic web data. To do this, it is urgently needed to extract ontology instances from structural and unstructured data to describe the internal content of the data, rather than metadata that is external information of the data, to the ontology.

As an example of the prior art for this purpose, there is Korea Patent No. 10-729103 (system and method for automatically extracting the ontology instance from an unstructured web document) of the method to extract the ontology instance from the structural and unstructured data. .

The prior art automatically extracts (creates) an instance of ontology necessary for domain ontology inference in real time or in real time from various information of unstructured web documents and databases existing on the Internet in ubiquitous environment and next generation web environment. The present invention relates to an ontology instance automatic extraction system and a method thereof.

The prior art (ontology instance extraction system), domain-specific information collecting means for collecting a web document corresponding to a domain, information extraction means for extracting information by the information extraction rule from the collected web document, and Information generating means for matching (selecting) semantic information matching the ontology schema from the extracted information, and converting the matched semantic information into data in an easy-to-search form, and easy to retrieve the converted semantic data. Storage means for storing and managing at one index, and information mapping means for mapping semantic data stored in the storage means to ontology based on predefined rules and using the ontology instance data.

As such, the related art uses a wrapper for extracting information using a predefined pattern or the like for a specific domain, or converts specific structural data into an instance.

However, the above-described conventional technology requires not only a lot of time to define rules for each domain, but also has an uneconomical disadvantage because it requires the use of different rules for specific domains. There is a problem, and since the ontology instance is extracted from each data without integration between the data extracted from the structural data and the data extracted from the unstructured data, the accuracy (precision) of the extracted ontology instance is inferior. It is a problem of the present invention to be solved.

Accordingly, the present invention uses ontology properties to extract a formal ontology instance in which a machine (for example, a computer program) can perform semantic based processing using structural properties and unstructured data. It is an object of the present invention to provide an instance extraction system and method thereof.

In addition, the present invention extracts an ontology instance using properties of the ontology from structural data and unstructured data, and integrates ontology-based triple data generated from the structural data and ontology-based triple data generated from the unstructured data. An object of the present invention is to provide an ontology instance extraction system and method using ontology properties for extracting ontology instances.

The objects of the present invention are not limited to the above-mentioned objects, and other objects and advantages of the present invention which are not mentioned above can be understood by the following description, and will be more clearly understood by the embodiments of the present invention. Also, it will be readily appreciated that the objects and advantages of the present invention may be realized by the means and combinations thereof indicated in the claims.

A system of the present invention for achieving the above object, Ontology instance extraction system, Ontology storage means for storing the pre-established ontology; Structural data analysis means for generating ontology-based triple data from the structural data using the ontology property of the ontology storage means; Unstructured data analysis means for generating ontology based triple data from unstructured data using the ontology property of the ontology storage means; And ontology instance extracting means for extracting ontology instances from the ontology-based triple data generated by the structural data analyzing means and the unstructured data analyzing means by using the ontology property of the ontology storing means.

The ontology instance extracting means further extracts the ontology instance to generate instance extension tripled data, and recursively stores the generated instance extension tripled data in the ontology storage means.

On the other hand, the method of the present invention for achieving the above object, Ontology instance extraction method, the ontology storage step of storing the pre-established ontology in the ontology storage means; A structural data analysis step of generating ontology-based triple data from structural data using the properties of the previously constructed ontology; An unstructured data analysis step of generating ontology-based triple data from unstructured data by using the properties of the built ontology; And an ontology instance extraction step of extracting an ontology instance from the ontology-based triple data generated in the structural data analysis step and the unstructured data analysis step by using the built-in ontology property.

The ontology instance extracting step may further include extracting the ontology instance to generate instance extension tripled data and recursively storing the generated instance extension tripled data in the ontology storage means.

As described above, the present invention has an effect of automatically extracting a formal ontology instance in which a computer program can perform semantic based processing from structural data and unstructured data.

In other words, the present invention can automatically extract ontology instances from the structural and unstructured data exploding through the Internet environment, so that computer programs can use the structured and unstructured data as information based on meaning. .

Accordingly, the present invention not only makes it possible to use structured data and unstructured data as semantic based information resources, but also provides relationships between them to provide information retrieval, resource sharing, information summary, document translation, personalization, resource management, And various application systems such as mashups can provide semantic based results.

In addition, the present invention automatically collects various vocabularies from structural data and unstructured data, assigns them to ontologies, and extends the ontology, thereby greatly helping the construction, management, expansion, and maintenance of the ontology.

BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, It can be easily carried out. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

First, the semantic web technology will be described in more detail to help understanding of the present invention.

Tim Berners-Lee first proposed the World Wide Web in 1989, marking the existing client-server architecture and HyperText Markup Language (HTML). The language allows users to post personal information anywhere on the Internet and to have a shared infrastructure for accessing that information through a browser. As a result, a great deal of information has been put on the Internet and distributed, and a great deal of information has existed on the Internet. The sharing of this information promotes social and technological development, and as a result, leads the innovation of the information society. Became.

However, the enormous amount of information has led to more and more efforts to find the information they want, and the emergence of various applications and services using the web has made it difficult and effective to find and use. Many difficulties have arisen.

In particular, the existing web-based search method is mainly searched by keywords, and the method of determining the priority of web documents using frequency of words or lexical information is mainly used to find a desired web document. There is a limit. In addition, it is very difficult to expand, integrate and share related web documents. This problem is because the existing web and markup languages are human-centered and focus on the expression technology of the web browser for humans to see and understand. As a result, the existing web is a human-centered information processing technology that does not provide enough functions for a computer to effectively extract, interpret, and process necessary information on its own.

Later, Semantic is a technology that can extend the existing web to realize semantic interoperability based on well-defined meanings that can be understood by computers, and to build an effective cooperative system between humans and computers. The web has come.

Tim Berners-Lee is not the concept of a new web that is completely different from the existing web, but rather extends the current web to give well-defined meaning to the information on the web, thereby helping computers and people work collaboratively. The role is defined as a paradigm that can be performed. The Semantic Web is designed to understand the meaning of information on the web, not only by people, but also by machines (computers) to provide intelligent services that meet the needs of users, or to facilitate collaboration between people and machines. It is a web with automatic service.

In other words, the semantic web is a next-generation web technology that enables a computer to understand, automate, integrate, and reuse the meaning of information resources.

1) Ontology

Ontology is a formal specification system for shared conceptualization and provides semantic information of domain vocabulary. Ontology is a kind of knowledge expression, and the computer can understand the concept represented by the ontology and process the knowledge. In order to deal with inferences, the ontology's axiom and rule system are needed.

2) semantically annotated web

A semantically annotated web is an ontology annotated web, which is a knowledge base. The Semantic Web can build a huge knowledge base that semantically integrates the distributed information resources of the Internet. In a narrow sense, it may be possible to build a knowledge base of information resources of a company or institution.

3) agent

An agent is an intelligent agent that collects, retrieves and infers information resources on behalf of a person (user), and exchanges information with other agents. Intelligent agents are the core of semantic web-based application systems.

The semantic web realizes semantic interoperability by using ontology and agent technology, and thus, the semantic web can leap from the information-based web to the knowledge-based semantic web.

1 is a configuration diagram of an ontology instance extraction system using an ontology property according to the present invention.

As shown in FIG. 1, the ontology instance extraction system using ontology properties according to the present invention uses ontology properties of the ontology repository 80 from ontology-based triple data from formal structural data 10 such as a database or a table. The ontology-based triple data 50 is generated from the unstructured data 30 such as a natural language document using the structural data analysis module 20 for generating (extracting) the ontology property of the ontology repository 80. From the unstructured data analysis module 40 for extraction and the ontology-based triple data 50 generated (extracted) from the structural data analysis module 20 and the unstructured data analysis module 40, Ontology instance for extracting the ontology instance by using the ontology property of the ontology repository 80 Extraction module 60.

At this time, the ontology instance extraction system using the ontology properties according to the present invention, to support the operation of each component, that is, to support the use of the ontology properties, ontology storage 80 that stores the built ontologies Equipped.

The ontology instance extraction module 60 extracts the ontology instance to generate instance extension triple data 70 and recursively stores the generated instance extension triple data 70 in the ontology storage 80. By further performing the function, the generated instance extension triple data 70 can recursively support the subsequent operation of each component.

Next, the structure and operation of the structural data analysis module 20, the unstructured data analysis module 40, and the ontology instance extraction module 60 will be described in detail.

First, the configuration and operation of the structural data analysis module 20 will be described in more detail.

The structural data analysis module 20 analyzes the structural data 10 generated through a formal language or standard and connects it with a built-on ontology of the ontology repository 80 to generate (extract) the ontology-based triple data 50. ) To the ontology instance extraction module 60.

Here, the ontology-based triple data 50 refers to data in which each entity is connected to the ontology while representing the data in a triple format of (Subject, Predicate, Object). For example, triple data such as (Hong Gil Dong, Age, 28) means that the property "Age" of the object "Hong Gil Dong" has a value of "28".

Therefore, in order to generate triple data from the structural data 10 such as a database, the value of the primary key field that can specify one record is “Subject”, each field name is “Predicate”, and the value of each field. You can make n triples (where n is a natural number) with "Object". For example, in any database table, a field exists like (ID, Name, Age, Gender), the primary key is ID, and the field value of one particular record is (0651, Kim Gil-dong, 28, M). When present together, this data can be interpreted and expressed as triple data to generate triple data such as (0651, name, Kim Gil-dong), (0651, age, 28), and (0651, gender, male).

In the triple data generated by the above method, the “Subject” and “Object” parts find and connect an appropriate instance corresponding to the meaning in the ontology store 80, and the “Predicate” part in the ontology store 80. The ontology-based triple data 50 may be generated by finding and connecting an appropriate property corresponding to the meaning.

Now, the configuration and operation of the unstructured data analysis module 40 will be described in more detail.

The unstructured data analysis module 40 analyzes unstructured data 30 such as a natural language document that is not generated through a standardized language or standard, and connects the ontology-based ontology of the ontology repository 80 to ontology-based triple data. Generate (50) and deliver it to the ontology instance extraction module 60.

Here, in order to generate the triple data from the unstructured data 30 such as a natural language document, a pre-built proper noun dictionary, a pre-built terminology dictionary, a known morpheme analysis, or a known entity name recognition method, etc. After extracting the entity name or semantic unit (unit having independent meaning as one object in the sentence) using at least one or more of these methods, triple data is generated using ontology properties, or Triple data is generated using methods such as syntax analysis, ontology properties, and triple pattern application. For example, when an arbitrary natural language sentence is "Lee Jun-gi was the main character of another 'Iljimae' to air in the second half of this year," expressed as triple data (MBC, aired, Iljimae), ( Triple data can be generated as shown in Iljimae, Main Character, Lee Joon Ki).

In the triple data generated by the above method, the “Subject” and “Object” parts find and connect an appropriate instance corresponding to the meaning in the ontology store 80, and the “Predicate” part in the ontology store 80. The ontology-based triple data 50 may be generated by finding and connecting an appropriate property corresponding to the meaning.

Now, the configuration and operation of the ontology instance extraction module 60 will be described in more detail.

The ontology instance extraction module 60 extracts a new ontology instance from the ontology-based triple data 50 using property information of an ontology repository 80 to generate instance extension triple data 70, and generates the generated instance. By further performing the function of recursively storing the extended triple data 70 in the ontology storage 80, the generated instance extended triple data 70 can recursively support the subsequent operation of each component. do.

Here, the ontology repository 80 is formed in a triple form, and is largely divided into a schema and a knowledge base. A schema represents the conceptual structure of knowledge, and has the form of (Class, Property, Class) or (Class, Property, Data Type). And the knowledge base is a part that represents actual data or knowledge, and has a form of (Instance, Property, Instance) or (Instance, Property, Data Value). For example, a schema consists of triple data representing a conceptual structure such as (drama, protagonist, actor), and a knowledge base consists of triple data representing actual data, such as (Iljimae, protagonist, Lee Joon-ki).

The ontology instance extraction module 60 extracts the ontology-based triple data 50 and the data are all tripled, and some data is connected to the ontology, but some data is not connected to the ontology.

At this time, the ontology instance extraction module 60 extracts triples in the ontology-based triple data 50 that “Predicate” is connected to a property of an ontology but “Subject” is not connected to an ontology. If "Subject" grouped the same triples, one "Subject" would have n "Predicates" associated with the ontology's properties and have n "Objects". These triples do not know which class they belong to, but their attributes represent known “Subjects”.

Then, the extracted triples are compared with the established ontology of the ontology reservoir 80. The extracted “Subject” extracts a class that has all n properties of the same triple compared to the previously constructed ontology of the ontology repository 80. At this time, if there is only one class that has all n properties, the extracted “Subject” of the extracted triple is extracted as an instance of the class. If there are two or more classes that have all n properties, check whether the class is defined through constraints. When a class is defined through a constraint, the extracted triple's “Subject” is used as an instance of the class only when the “Object” value of the extracted triples satisfies the constraint. Extract and generate instance extension triple data 70. In other words, it extracts which class of ontology the instance is based on the property of the object that can be identified through the ontology property.

On the other hand, since the ontology based triple data 50 is generated through the structural data analysis module 20 and the unstructured data analysis module 40, the ontology instance extraction module 60 generates data generated through different data sources. ) Can be integrated into one to accurately extract ontology instances.

In general, structural data, such as databases, are objects whose typical properties do not change (for example, a person's name, date of birth, affiliation, etc.), while unstructured data is a temporary or new property of an object (eg entertainers). New appearances, wedding announcements, and transfers of athletes' teams are often targeted. Therefore, an object that is difficult to know as an ontology instance because the class is difficult to know through any one data source can be extracted as an ontology instance by grasping the class through the properties of objects extracted from different data sources. . For example, if the class named "Actor" is defined in the ontology schema that "Affiliation" is a "Broadcaster" Class, and "Appears" in a "Drama" Class, The object can be extracted from the structural data called "MBC" called "broadcaster" from the character DB, and from the unstructured data called news document that it can "appear" to the "drama" called "Iljimae". If so, the property of the object named "junki" extracted from the two data sources indicates that it is a "actor" class.

Next, a method of extracting an ontology instance using an ontology property by the system according to the present invention will be briefly described with reference to FIG. 2.

2 is a flowchart illustrating an ontology instance extraction method using ontology properties according to the present invention.

First, in order to support the use of the ontology property, the ontology storage 80 stores the pre-established ontology (201).

Thereafter, the structural data analysis module 20 generates (extracts) the ontology-based triple data 50 from the structural data 10 by using the ontology properties of the previously constructed ontology.

That is, the structural data analysis module 20 interprets the structural data 10 generated through the formalized language or standard, and connects the ontology-based triple data 50 by connecting with the already built ontology of the ontology repository 80. Create (extract).

The unstructured data analysis module 40 generates (extracts) the ontology-based triple data 50 from the unstructured data 30 by using the ontology properties of the previously constructed ontology.

That is, the unstructured data analysis module 40 analyzes unstructured data 30 such as a natural language document that is not generated through a formal language or standard, and connects the ontology ontology of the ontology repository 80 to ontology. The base triple data 50 is generated (extracted).

Thereafter, the ontology instance extraction module 60 generates ontology properties of the built ontology from the ontology-based triple data 50 generated (extracted) by the structural data analysis module 20 and the unstructured data analysis module 40. The ontology instance is extracted using the generated instance extension triple data 70 (204).

In addition, the ontology instance extraction module 60 additionally performs a function of recursively storing the generated instance extension triple data 70 in the ontology storage 80 (205), thereby expanding the generated instance. Triple data 70 enables recursively to support the subsequent operation of each component.

On the other hand, the method of the present invention as described above can be written in a computer program. And the code and code segments constituting the program can be easily inferred by a computer programmer in the art. In addition, the written program is stored in a computer-readable recording medium (information storage medium), and read and executed by a computer to implement the method of the present invention. The recording medium may include any type of computer readable recording medium.

The present invention described above is capable of various substitutions, modifications, and changes without departing from the technical spirit of the present invention for those skilled in the art to which the present invention pertains. It is not limited by the drawings.

The present invention can be used in semantic based information systems such as semantic search systems, semantic annotation systems, and semantic metadata extraction systems.

1 is a configuration diagram of an ontology instance extraction system using an ontology property according to the present invention;

2 is a flowchart illustrating an ontology instance extraction method using ontology properties according to the present invention.

* Explanation of symbols on the main parts of the drawing

10: structural data 20: structural data analysis module

30: unstructured data 40: unstructured data analysis module

50: ontology based triple data 60: ontology instance extraction module

70: instance extension triple data 80: ontology repository

Claims (11)

In the ontology instance extraction system, Ontology storage means for storing the constructed ontology; Structural data analysis means for generating ontology-based triple data from the structural data using the ontology property of the ontology storage means; Unstructured data analysis means for generating ontology based triple data from unstructured data using the ontology property of the ontology storage means; And Ontology instance extracting means for extracting ontology instances from ontology-based triple data generated by the structural data analyzing means and the unstructured data analyzing means by using the ontology properties of the ontology storing means Ontology instance extraction system comprising a. The method of claim 1, The ontology instance extraction means, And extracting the ontology instance to generate instance extension tripled data, and recursively storing the generated instance extension tripled data in the ontology storage means. The method according to claim 1 or 2, The ontology instance extraction means, Ontology instance extraction system characterized in that to extract the ontology instance by integrating the ontology-based triple data generated by the structural data analysis means and the unstructured data analysis means through different data sources into one. The method of claim 3, wherein The ontology instance extraction means, After analyzing the ontology-based triple data generated by the structural data analysis means and the non-structural data analysis means to grasp the property, the new ontology instance is extracted using property information by comparing with the ontology of the ontology storage means. Ontology instance extraction system, characterized in that. The method of claim 3, wherein The structural data analysis means, Ontology instance extraction system, characterized in that to generate the ontology-based triple data by analyzing the structural data to generate the triple data and connected with the instance and properties of the pre-established ontology of the ontology storage means. The method of claim 3, wherein The unstructured data analysis means, Ontology instance extraction system, characterized in that to generate the ontology-based triple data by analyzing the unstructured data to generate the triple data and connected with the instance and properties of the pre-established ontology of the ontology storage means. In the ontology instance extraction method, An ontology storage step of storing the constructed ontology in an ontology storage means; A structural data analysis step of generating ontology-based triple data from structural data using the properties of the previously constructed ontology; An unstructured data analysis step of generating ontology-based triple data from unstructured data by using the properties of the built ontology; And An ontology instance extraction step of extracting an ontology instance from the ontology-based triple data generated in the structural data analysis step and the unstructured data analysis step by using the properties of the built ontology. Ontology instance extraction method comprising a. The method of claim 7, wherein The ontology instance extraction step, And extracting the ontology instance to generate instance extension tripled data, and recursively storing the generated instance extension tripled data in the ontology storage means. 9. The method according to claim 7 or 8, The ontology instance extraction step, The ontology instance extraction method of extracting an ontology instance by integrating ontology-based triple data generated in the structural data analysis step and the unstructured data analysis step through different data sources into one. The method of claim 9, The structural data analysis step, The ontology instance extraction method of generating ontology-based triple data by interpreting structural data, generating triple data, and connecting the pre-established instance and property of the ontology. The method of claim 9, The unstructured data analysis step, The ontology instance extraction method of generating ontology-based triple data by interpreting unstructured data, generating triple data, and connecting the pre-established instance and property of the ontology.

Images