KR101540430B1 - Bridge ontology processing apparatus and method for mapping continuty - Google Patents

Abstract

The bridge ontology processing apparatus for continuity of mapping according to the present invention generates a generalized mapping using a standard ontology of a source standard concept and an object standard concept, extracts a relationship between a source standard concept and an object standard, An accuracy mapping engine section for generating matching information including a conformed concept or a non-conformed concept in which a structure of a source standard concept and a target standard coincides with a standard ontology, a generalized mapping A mapping storage unit for storing the personalized mapping and matching information, and a continuity mapping unit for searching for matching concepts corresponding to the mismatch concept among the matching information stored in the mapping storing unit and generating modified mappings based on the detected matching concept and mismatch concept .

Description

[0001] BRIDGE ONTOLOGY PROCESSING APPARATUS AND METHOD FOR MAPPING CONTINUTIVE [0002]

The present invention relates to ontology mapping, and more particularly, to ontology mapping in a healthcare domain.

The usefulness of the Detailed Clinical Model (DCM) is an international collaborative study of the CIMI (Clinical Information Modeling Intiative) that contributes to providing a common format for detailed descriptions of health information content Can be confirmed. Depending on the activities of these CIMIs, interoperability information can be semantically generated and shared by health records, messages and documents. It is based on an ontology for generating Semantic Mapping for a detailed clinical model. However, conventional ontology mapping does not adequately address data-level interoperability.

Tao, Cui, C. G. Parker, T. A. Oniki, J. Pathak, S. M. Huff, and C. G. Chute, "An OWL meta-ontology for the clinical element model." In AMIA Annual Symposium Proceedings, vol. 2011, p. 1372. The American Medical Informatics Association deals with the medical factor model. The Clinical Elemental Model described here is a logical model used to represent medical information for the Electronic Health Record system. These medical factor models are defined by the same concept as by the detailed clinical model. An approach to defining a meta ontology for describing a medical element model in the OWL format is proposed in the paper mentioned above. Although this approach is defined as a top-level ontology, there is a problem in that it requires a personalization problem set in the system in the process of processing feature information of the structure.

In 2011, T. Kirsten, A. Gross, M. Hartung, and E. Rahm, Journal of biomedical semantics 2, no. 6 discusses an ontology response tool that determines evolutionary ontology changes in the mapping process. The GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution. This system meets a huge biomedical ontology. However, both of these mappings and changes deal only with generalized mappings, but do not consider customized mappings.

Tao, Cui, C. G. Parker, T. A. Oniki, J. Pathak, S. M. Huff, and C. G. Chute. "An OWL meta-ontology for the clinical element model." In AMIA Annual Symposium Proceedings, vol. 2011, p. 1372. American Medical Informatics Association T. Kirsten, A. Gross, M. Hartung and E. Rahm, "GOMMA: a component-based infrastructure for managing and analyzing life sciences and their evolution." Journal of biomedical semantics 2, no.

SUMMARY OF THE INVENTION It is an object of the present invention to provide an apparatus and method for achieving interoperability through integrated personalized mapping and generalized mapping in order to ensure accuracy of mapping stored in an ontology.

The bridge ontology processing apparatus for continuity of mapping according to the present invention generates a generalized mapping using a standard ontology of a source standard concept and an object standard concept, extracts a relationship between a source standard concept and an object standard, An accuracy mapping engine section for generating matching information including a conformed concept or a non-conformed concept in which a structure of a source standard concept and a target standard coincides with a standard ontology, a generalized mapping A mapping storage unit for storing the personalized mapping and matching information, and a continuity mapping unit for searching for matching concepts corresponding to the mismatch concept among the matching information stored in the mapping storing unit and generating modified mappings based on the detected matching concept and mismatch concept .

The continuity mapping unit transforms the generated modified mapping into a bridge ontology format and stores it in a mapping storage unit. In addition, the bridge ontology includes a string match bridge, a synonym bridge, a hyponym bridge, a polysemous bridge, a label bridge, an overlap bridge, A Children Based Structural Bridge (CBSB), and a Property Based Structural Bridge (PBSB).

The method for processing a bridge ontology for mapping continuity according to an embodiment of the present invention first generates a generalized mapping using a source standard concept and a standard ontology of the object standard concept. Then, the relationship between the source standard concept and the target standard is extracted to generate a personalized mapping, and the structure of the source standard concept and the object standard is defined as a Conformed Concept or a Non-Conformed Concept that coincides with the standard ontology Concept). ≪ / RTI > Next, the matching concept corresponding to the concept of inconsistency is searched among the previously stored matching information, and the modified mapping is generated through the concept of matching and inconsistency found.

The ontology can be evolved through the bridge ontology processing apparatus and method for continuity of the mapping according to the present invention, and the continuity of mapping can be achieved by neutralizing the influence of the personalized mapping in the generalized mapping.

1 is a configuration diagram showing an embodiment of a bridge ontology processing apparatus for continuity of mapping according to the present invention.
2 is a flowchart illustrating a method of processing a bridge ontology for continuity of mapping according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. The terms and words used in the present specification are selected in consideration of the functions in the embodiments, and the meaning of the terms may vary depending on the intention or custom of the invention. Therefore, the terms used in the following embodiments are defined according to their definitions when they are specifically defined in this specification, and unless otherwise specified, they should be construed in a sense generally recognized by those skilled in the art.

1 is a configuration diagram showing an embodiment of a bridge ontology processing apparatus for continuity of mapping according to the present invention.

Referring to FIG. 1, a bridge ontology processing apparatus 100 for continuity of mapping according to the present invention includes an accuracy mapping engine unit 110, a mapping storage unit 130, and a continuity mapping unit 140.

The accuracy mapping engine unit 110 includes a generalization mapping module 111 and a personalization mapping module 112 and deals with the accuracy of mapping generated between different standards.

The accuracy mapping engine unit 110 uses both the generalization mapping module 111 and the personalization mapping module 112 to ensure a higher accuracy level. Then, the accuracy mapping engine unit 110 performs a mapping between a source standard concept and a target standard concept. The source standard concept and the target standard concept represent the concept (source standard concept) for starting the mapping and the concept (target standard concept) mapped to the source standard concept in the ontology mapping.

Two standard ontologies 1111 and 1112 of the generalization mapping module 111 are input to the general mapping engine 1113 to generate a generalized mapping. The general mapping engine 1113 represents a system for solving the parallel heterogeneity in an ontology correspondence process. The generic mapping engine 1113 treats it as an input ontology in the OWL format and uses different Bridge Algorithms to generate generalized mappings between the two standard ontologies 1111 and 1112.

Bridge algorithms can be used for various applications such as String Match Bridge, Synonym Bridge, Hyponym Bridge, Polysemous Bridge, Label Bridge, Overlap Bridge, A Children Based Structural Bridge (CBSB), and a Property Based Structural Bridge (PBSB).

The generalized mappings generated by the generic mapping engine 1113 have a high level of accuracy, but the structure still coincides with some concepts and is inconsistent with the rest of the concepts based on their needs. This will cause a change in the state of some of the mappings, such as the Stale Mapping, in the Bridge ontology. Such matching information is handled by the personalization mapping module 112. The matching information is maintained by the P-DCM ontology unit 1123 representing the P-DCM. The matching information includes information about the concept of consistency and the concept of inconsistency of the ontology. The P-DCM ontology unit 1123 is based on matching information and matching information on the mismatch concept. The matching information regarding the matching concept and the mismatch concept is the responsibility of the matching management unit 1122. In addition, the concept annotated with the information is extracted by the annotation information extraction unit 1121. [

That is, the mapping engine unit 110 extracts the concept of agreement from the generalized mapping, and extracts a concept that does not correspond to the structure of the standard ontology among the concepts extracted from the personalized mapping as a concept of inconsistency.

The annotation information extracting unit 1121 extracts the information of the P-DCM concept and extracts the relationship between the source standard concept and the object standard concept stored as an annotation on the ontology. This causes the generation of the personalized mapping in the bridge ontology storage unit 132. [ The personalized corresponding bridge is used for classification as a type of mapping in the bridge ontology storage 132.

The mapping storage unit 130 stores the personalized mapping by the generalized mapping and personalization mapping module 112 to the generalization mapping module 111 of the accuracy mapping engine unit 110. The generalized and personalized mappings stored in the mapping storage unit 130 are stored in the bridge ontology storage unit 132 in a structured bridge ontology format. To transform from one standard format to another, the bridge ontology storage 132 uses the mapping to transform the format.

The continuity mapping unit 140 manages the continuity of the mapping and the system change. The continuity mapping unit 140 searches the bridge ontology storage unit 132 for a matching concept corresponding to the extracted mismatch concept. Then, the modified mapping is generated through the retrieved matching concept and the extracted mismatch concept. The generated modified mapping is transformed into a bridge ontology format and stored as a personalized mapping in the bridge ontology storage.

The change detecting unit 141 obtains the matching information from the consistency managing unit 1122. [ The change detection unit 141 compares the information stored in the bridge ontology storage unit 132 with the matching information to identify a mapping composed of a non-conformed concept. The change detection unit 141 stores the existing mapping generated by matching the matching information of the coincidence management unit 1122 and the information of the bridge ontology storage unit 132 in the existing mapping (Stale Mapping) storage unit 144 . The existing mapping stored in the existing mapping storage unit 144 is transferred to the change collection unit 142.

Change detection algorithm Input: CCi, NCCj / * where CCi, NCCj are Conformed Concepts and Non-Conformed Concepts respectively * /
OutPut: M (CCi↔NCCj) / * where M (CCi↔NCCj) are the mapping in the Bridge Ontology between Conformed and Non-Conformed Concepts * /

1 begin
2 Conformance Manager: CM
3 CM ← CCi ∧ NCCj / * 11 * /
4 Change Detector: CD
5 Bridge Ontology: BO
6 Stale Mapping: SM
7 CD ← getNonConformedConcepts (CM) / * 12 * /
8 for NCCj in CD do
9 CD ← Find (M (CCi↔NCCj) in BO) / * 13 * /
10 if ∃M (CCi↔NCCj) then
11 SM ← LABEL M (CCi↔NCCj) ≡Deferred
12 STORE SM / * 14 * /
13 end
14 else
15 Goto 8
16 end
17 end
18 end

Table 1 shows one embodiment of the change detection algorithm of the change detection unit 141. [ The change collection algorithm represents a mechanism for identifying changes based on personalized mapping and identifying existing mappings. The matching management part (CM) 1122 provides a concept of CCi matching and a concept of NCCj mismatch like CM? CCi? NCCj (11). The mismatch concept NCCj is extracted from the coincidence management unit 1122 as CD ← getNonConformedConcepts (CM) 12 for the change detection unit 141. The mismatch concept mapping stored in the bridge ontology storage unit (BO) 132 can be identified by CD? Find (M (CCi? NCCj) in BO) The identified mappings are considered an existing mapping (SM) and are stored in the existing mapping storage 144 as in STORE SM 14. [

The change collection unit 142 based on the existing mapping information collects information on the conformed concept. The change collecting unit 142 receives the annotated mapping from the annotation information extracting unit 1121. The change collection unit 142 finds a modified mapping and a new mapping from the annotated mapping.

Change collection algorithm for modified mappings Input: SM / * where SM reflects Stale Mapping from Change Detector * /
Output: △ M (CCi↔NCCj) / * where △ M (CCi↔NCCj) are the modified mapping added in the Bridge Ontology * /

1 begin
2 Change Collector: ChC
3 Target Annotated Mapping: TAM
4 Target Standard Ontology Conformed Concept: CCj
5 Modified Mapping: MM
6 CCi ← getConformedConcepts (SM) / * 21 * /
7 ChC ← annotatedMapping (CCi) / * 22 * /
8 for CCi in SM do
9 TAM ← FindCorrespondingAnnotatedMapping (ChC) / * 23 * /
10 CCj ← TAM.getTargetConformedConcept (CCi) / * 24 * /
11 GENERATE △ M (CCi↔NCCj) / * 25 * /
12 end
13 MM ← STORE △ M (CCi↔NCCj) / * 26 * /
14 end

Table 2 shows the change collection algorithm of the change collection unit 142. The matching concept CCi is received from the existing mapping storage 144 as CCi < - > getConformedConcepts (SM) The change collection unit (ChC) 142 identifies the annotated mapping based on the matching concept CCi depicted as ChC < - > annotated Mapping (CCi) (22). Identified annotated mappings are used in Target Annotated Mapping (TAM) to find matching concepts such as TAM ← FindCorrespondingAnnotatedMapping (ChC) (23). After finding the target annotation mapping (TAM), the TargetConformedConcept (CCi) is used to find the target standard ontology conformed concept (CCj) to find CCj ← TAM.getTargetConformedConcept (CCi) . The modified mapping (MM) is stored in the modified mapping storage unit 146, such as MM ← STORE M (CC i↔NCCj) 26.

Change collection algorithm for new mapping Input: PDCOM / * where PDCMO represents Personalized Detailed Clinical Model Ontology * /
Output: NAM (CCi↔CCj) / * where NAM (CCi↔CCj) are the New Annotated Mapping added in the Bridge Ontology * /

1 begin
2 Annotated Mapping: AM New Annotated Mappings: NAM Source Conformed
Concept: SCCi Target Conformed Condept: TCCj Personalized Detailed
Clinical Model Ontology: PDCMO Bridge Ontology: BO
3 AMi ← SCCi≡TCCj / * 31 * /
4 for AMi? PDCMO do
5 Search (BO)
6 if AMi ≠ M (SCCi↔TCCj) then
7 GENERATE NAM (CCi↔CCj) / * 32 * /
8 end
9 else
10 GOTO 8
11 end
12 end
13 NAM ← STORE NAM (CCi↔CCj) / * 33 * /
14 end

Table 3 shows a change collection algorithm for the new mapping of the change collection unit 142. The change collecting unit 142 identifies the new mapping through the algorithm of Table 3 and stores the identified new mapping in the bridge ontology storage unit 132. The P-DCMO consists of a new annotated mapping that is not already part of the bridge ontology. The new annotated mapping means the new mapping. This new annotated mapping (AM) constitutes the Source Matching Concept (SCCi) mapping with the Target Matching Concept (TCCi) depicted as AMi ← SCCi≡TCCj (31). The bridge ontology (BO) is confirmed by the presence of this mapping. If such a new annotated mapping (NAM) is missed in the bridge ontology storage 132, a new annotated mapping (NAM) is generated as GENERATE NAM (CCi ⊂ CCj) 32. Finally, after this new annotated mapping (new mapping) is created, the new annotated mapping is stored in the new mapping storage 147 as NAM < - > STORE NAM (CCi &

As such, the modified mapping is passed to the modified mapping storage 146, and the new mapping is passed to the new mapping storage 147 and stored. Then, the modified mapping of the modified mapping storage unit 146 and the new mapping of the new mapping storage unit 147 are transferred to the format transformation unit 143.

The format transforming unit 143 transforms the received annotated mapping and the modified mapping into a bridge ontology (BO) format of the bridge ontology storage unit 132 and transfers the transformed mapping format to the BO format storage unit 148.

Format transformation algorithm Input: MM, NAM / * where MM and NAM represent Modified Mapping and New Annotated Mapping respectively * /
Output: / * Customized Mapping Result * /

1 begin
2 for MMi∈MM do
3 bridge: = "Customized Matching Bridge"
4 Ci: = Source Ontology Concept that belongs to MMi
5 Cj: = Target Ontology Concept that belongs to MMi
6 PopulateBridgeOntology (Ci, Cj, bridge) / * 41 * /
7 end
8 for NAMi∈NAM do
9 bridge: = "Customized Matching Bridge"
10 Ci: = Source Ontology Concept that belongs to NAMI
11 Cj: = Target Ontology Concept that belongs to NAMI
12 PopulateBridgeOntology (Ci, Cj, bridge)
13 end
14 end

Table 4 shows the format transforming algorithm of the format transforming unit 143. The format transformation algorithm is an algorithm for transforming the modified mapping generated in the change collection unit 142 and the new mapping into the bridge ontology format for storing in the bridge ontology storage unit 132. [ All modified mappings (MMi) are stored in the bridge ontology storage 132 together with the source ontology concept and the target ontology concept. The new annotated mapping (NAMi), such as the source ontology concept and the target ontology concept, is also stored in the bridge ontology storage 132. These mappings are stored under a category of personalized corresponding bridges. The ontology then adds new and modified mappings such as PopulateBridgeOntology (Ci, Cj, bridge) (41).

The BO format storage unit 148 transfers the annotated mapping and the modified mapping transformed into the bridge ontology format to the bridge ontology storage unit 132 via the personalized mapping unit 133 and stores the modified mapping.

2 is a flowchart illustrating a method of processing a bridge ontology for continuity of mapping according to an embodiment of the present invention.

Referring to FIG. 2, a bridge ontology processing method for mapping continuity according to an exemplary embodiment of the present invention creates a generalized mapping using a standard ontology (201). Generalized mapping Generates a generalized mapping based on two standard ontologies of the module. The process of generating generalized mappings between two standard ontologies uses different bridge algorithms.

Then, matching information including a matching concept and a mismatch concept is generated (202). The generalized mappings generated by the generic mapping engine have a high level of accuracy, but the structure still coincides with some concepts and is inconsistent with the rest of the concepts based on their needs. This will cause a change in the state of some of the mappings, such as the Stale Mapping, in the Bridge ontology. Such matching information is handled by the personalization mapping module. The matching information includes information about the concept of consistency and the concept of inconsistency of the ontology. The P-DCM ontology part is based on the conformance concept and the matching information on the concept of inconsistency. Consistency information on the concept of coincidences and inconsistencies is the responsibility of the coincident management. The concept of annotating the information is extracted by the annotation information extracting unit.

In other words, the concept of consensus is extracted from the generalized mapping, and concepts that do not correspond to the standard ontology structure are extracted as the concept of inconsistency among the concepts extracted from the personalized mapping.

Next, the relationship between the source standard concept and the target standard is extracted to generate a personalized mapping (203). The annotation information extraction unit extracts the information of the P-DCM concept and extracts the relationship between the source standard concept and the object standard concept stored as an annotation on the ontology. This results in the creation of a personalized mapping in the bridge ontology store. The personalized corresponding bridge is used for classification as a type of mapping in the bridge ontology store.

The generated generalized and personalized mappings are then stored (204). The mapping store stores generalized and personalized mappings. The generalized and personalized mappings stored in the mapping store are stored in a structured bridge ontology format in the bridge ontology store. To transform from one standard format to another, the bridge ontology store uses a mapping to transform the format.

Next, the matching concept corresponding to the concept of inconsistency is searched for among the previously stored matching information, and the modified mapping is generated through the concept of matching and inconsistency (205). The continuity mapping unit searches the bridge ontology storage unit for the matching concept corresponding to the extracted mismatch concept. Then, the modified mapping is generated through the retrieved matching concept and the extracted mismatch concept. The generated modified mapping is transformed into a bridge ontology format and stored as a personalized mapping in the bridge ontology storage. First, the matching information is obtained from the matching management unit, and the information stored in the bridge ontology storage unit is compared with the matching information to identify the mapping made by the concept of inconsistency. Then, the mismatch concept is compared with the previously stored matching information, and the generated existing mapping is stored in the existing mapping storage unit. The concrete method for detecting the change by comparing the concept of discrepancy and the stored log information is the algorithm described in Table 1 described above.

Then, the retrieved matching concept and the mismatch concept are used to generate the modified mapping. The change collector based on existing mapping information collects information about the concept of matching. And receives the annotated mapping from the annotation information extraction unit to generate a modified mapping. The specific procedure for generating the modified mapping is based on the change collection algorithm described in Table 2 above.

Once the modified mapping is created, the generated modified mapping is transformed into the bridge ontology format (206). The bridge ontology storage unit stores the received mapping in a bridge ontology format. Therefore, the modified mapping is transformed into the bridge ontology format and transmitted to the bridge ontology storage unit. The method of transforming the modified mapping into the bridge ontology format refers to the format transformation algorithm described in Table 4 described above.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, This is possible.

100: Bridge ontology processing device for mapping continuity
110: Accuracy mapping engine part
111: Generalized mapping module
112: Personalization mapping module
130:
131: generalized mapping unit
132: Bridge ontology storage unit
133: Personalized mapping unit
140: continuity mapping unit
141:
142:
143: Format transformation section

Claims (8)

An ontology processing apparatus for mapping between a source standard concept and an object standard concept,
Generating a generalized mapping using the source standard concept and the standard ontology of the object standard concept, extracting a relationship between the source standard concept and the object standard to generate a personalized mapping, An accuracy mapping engine unit for generating matching information including a conformed concept or a non-conformed concept in which the structure of the standard matches the standard ontology;
A mapping storage for storing the generalized mapping, the personalized mapping, and the matching information; And
A continuity mapping unit searching for a matching concept corresponding to the mismatch concept among the previously stored matching information in the mapping storage unit and generating a mapped modified through the searched matching concept and the mismatch concept;
Wherein the bridge ontology processing unit is configured to process the bridge ontology for continuity of mapping. The method according to claim 1,
Wherein the continuity mapping unit transforms the generated modified mapping into a bridge ontology format and stores the modified modified mapping in the mapping storage unit. 3. The method of claim 2,
The bridge ontology may include a String Match Bridge, a Synonym Bridge, a Hyponym Bridge, a Polysemous Bridge, a Label Bridge, an Overlap Bridge, and a Child And a property based structural bridge (PBSB). The system according to claim 1, wherein the bridge based ontology is a bridge structure. The method according to claim 1,
Wherein the accuracy mapping engine unit extracts annotation information from the personalized mapping. ≪ Desc / Clms Page number 21 > A method of processing an ontology processing apparatus for continuity of mapping between a source standard concept and an object standard concept,
Generating a generalized mapping using the source standard concept and the standard ontology of the object standard concept;
Wherein the source standard concept and the object standard are structured by extracting a relationship between the source standard concept and the target standard to generate a personalized mapping, and the concept of the source standard and the structure of the target standard are classified into a conformed concept or an inconsistent inconsistency concept The method comprising: generating matching information including a plurality of consonants; And
Searching for a matching concept corresponding to the mismatch concept among the preexisting matching information, and generating a mapped modified through the searched matching concept and the mismatch concept;
Wherein the bridge ontology processing method comprises the steps of: 6. The method of claim 5,
Storing the generalized mapping, the personalized mapping and the matching information;
Further comprising the steps of: (a) providing a bridge ontology for continuity of mapping. The method according to claim 6,
Transforming the generated modified mapping into a bridge ontology format;
Further comprising the steps of: (a) providing a bridge ontology for continuity of mapping. 6. The method of claim 5,
And extracting annotation information from the personalized mapping. ≪ Desc / Clms Page number 19 >

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